experiment social media stipendium

Experiment e.V. – Social Media Stipendium

Angaben zum auslandsstipendium, das social media stipendium von experiment e.v..

Experiment e.V. vergibt Teilstipendien an Schüler, die gerne mit Experiment ins Ausland gehen würden und während ihres Auslandsaufenthalts auf Social Media darüber berichten möchten. Dabei sind alle Programmländer und -längen möglich, wobei ein Teilstipendium von maximal 1.000 € vergeben wird.

Die Auswahlkriterien

• Auslandsaufenthalt: Du möchtest gerne mit Experiment ins Ausland gehen – das Land und die Dauer spielen dabei keine Rolle.
• Social Media: Du hast eine Schwäche für Social Media und postest gerne auf TikTok, Instagram oder YouTube .
• Videos: Du hast kein Problem damit, Dich vor der Kamera zu präsentieren und bist dazu bereit, während des Schüleraustauschs regelmäßig von Deinen Erfahrungen auf den Social Media Accounts von Experiment zu berichten.
• Alter: Du bist mindestens 14 Jahre alt – vorher sind Auslandsaufenthalte mit Experiment nicht möglich.

Die Bewerbung

Sende Deine Bewerbung per E-Mail an Svenja Kopp.

Möchtest Du Deinen Auslandsaufenthalt im Sommer beginnen, dann bewirb Dich bis zum 31.12. des Jahres zuvor, willst Du hingegen im Winter ausreisen, dann ist die Bewerbungsfrist am 31.07. Beachte, dass Du Dich, bevor Du Dich für das Stipendium bewirbst, erstmal für ein Auslandsprogramm bei Experiment e.V. bewerben und dort die Bewerbungsschritte, inklusive Kennenlerngespräch , durchlaufen musst.

Bewerbungsunterlagen

• Video von Dir (z.B. YouTube-Vlog aus Deinem Alltag, Instagram Story oder Reel oder TikTok-Video), max. 3 Minuten
• Kurzvideo, in dem Du erzählst, wieso Du mit Experiment ins Ausland gehen willst und warum Du der Richtige für das Stipendium bist
• Anschreiben

Die perfekte Stipendienbewerbung

Unser Tipp: Nutze Muster und Formulierungsbaukasten , um Dich von anderen Bewerbern abzuheben.

Jetzt mit Erfolg bewerben

Experiment e.V.: Weitere Stipendienprogramme

• Experiment e.V. – Teilstipendien der Bürgerstiftung Leverkusen
• Experiment e.V. – Peter Ackermann-Teilstipendien
• Experiment e.V. – Klima-Stipendium
• Experiment e.V. – Experiment-Teilstipendien
• Experiment e.V. – Europa-Stipendium

Ähnliche Stipendien

• Behörde für Schule und Berufsbildung Hamburg – Förderung eines Schulbesuchs im Ausland
• Kreuzberger Kinderstiftung – Stipendium Erlebe die Welt!
• zis Stiftung für Studienreisen – Reisestipendium
• Partnership International e.V. – Stipendien 2. HJ für Schüleraustausch
• Bayerisches Kultusministerium – Botschafter Bayerns

Austauschorganisation Experiment

• Programme und Kurzbewerbung
• Bewertungen

Europa-Stipendium

Art des Stipendiums

Teilstipendium für alle europäischen Programmländer für die Dauer eines Schulhalbjahres oder Schuljahres

Wert des Stipendiums

bis zu 1000 Euro

Bewerbungszeitpunkt

• 31.12. (Sommerausreise im Folgejahr)
• 31.07. (Winterausreise im Folgejahr)

Stipendium der Kreuzberger Kinderstiftung

Teilstipendium für alle Programme/Länder mit einem Programmpreis von maximal 13.500 Euro

10 bis 20 Stipendien im Jahr

Richtet sich nach dem Familieneinkommen, bis zu 80% der Programmkosten.

frühzeitig, bevor dem Beginn des Programms

Peter Ackermann-Stipendium

Teilstipendium für alle Programme/Länder, besonders förderungswürdig sind europäische Zielländer

Bis zu 40 Stipendien im Jahr.

Höhe richtet sich nach dem Familieneinkommen.

• bis 1.3. eines Jahres

Palamentarisches Patenschaftsprogramm

Vollstipendium

300 (insgesamt)

Programmpreis des USA-Classic-Programm.

Mai bis September

Klima-Stipendium

Teilstipendium für alle Programmländer für die Dauer eines Schulhalbjahres oder Schuljahres

bis zu 500 Euro

Social Media Stipendium

Last updated 20/06/24: Online ordering is currently unavailable due to technical issues. We apologise for any delays responding to customers while we resolve this. For further updates please visit our website: https://www.cambridge.org/news-and-insights/technical-incident

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings .

Login Alert

• > Journals
• > Political Science Research and Methods
• > Volume 7 Issue 3
• > How Does Media Influence Social Norms? Experimental...

Article contents

Media and the microfoundations of social norms change, unesco’s campaign: a media intervention in san bartolomé quialana, research design, empirical strategy, how does media influence social norms experimental evidence on the role of common knowledge.

Published online by Cambridge University Press:  20 February 2018

• Supplementary materials

How does media influence beliefs, attitudes, and behaviors? While many scholars have studied the effect of media on social and political outcomes, we know surprisingly little about the channels through which this effect operates. I argue that two mechanisms can account for its impact. Media provides new information that persuades individuals to accept it (individual channel), but also, media informs listeners about what others learn, thus facilitating coordination (social channel). Combining a field experiment with a plausibly natural experiment in Mexico, I disentangle these effects analyzing norms surrounding violence against women. I examine the effect of a radio program when it is transmitted privately versus when it is transmitted publicly. I find no evidence supporting the individual mechanism. The social channel, however, increased rejection of violence against women and increased support for gender equality, but unexpectedly increased pessimism about whether violence would decline in the future.

A central concern across social sciences has been to understand the extent to which mass communication can influence social and political outcomes. Indeed, many scholars have shown that media effects abound and cover a wide area of topics, anywhere from political support and electoral behavior up to the perpetration of violence. However, we know little about the underlying mechanisms behind these effects. That is, how is it that media influence beliefs, attitudes, and behaviors? In particular, how does media influence social norms?

The process underlying media influence can be broadly decomposed into two potential effects: (1) an individual or direct effect, and (2) a social or indirect effect. In the former, media provides information about new norms and persuades individuals to accept them (Bandura Reference Bandura 1986 ; DellaVigna and Gentzkow Reference DellaVigna and Gentzkow 2010 ). In the latter, the information provided also serves as a coordination device. Coordination is needed because one can conceptualize social norms as coordination problems, that is, situations in which each person wants to participate only if others participate as well (Mackie Reference Mackie 1996 ; Chwe Reference Chwe 1998 ). As such, the provision of public information can enhance coordination on that norm through the creation of common knowledge (Mackie Reference Mackie 1996 ; Chwe Reference Chwe 2001 ).

While the individual mechanism would have an effect regardless of the dissemination method, the social one would be stronger when dissemination has a public component. Hence, I argue that information has a differential effect when it is transmitted individually and privately (e.g., through leaflets) than when it is transmitted through more social or collective outlets (such as mass media or public meetings). That is, how information is provided is important to fully understand the mechanisms behind its influence. Critically, however, media itself has a public component, and media related interventions in the literature have naturally been public. As such, by design, media is able to induce common knowledge precluding the isolation of the social component from the individual one, and thus making the task of fully understanding the microfoundations of media influence a daunting one.

This paper fills this gap by disentangling the extent to which media influence acts through the individual mechanism (via persuasion) versus the extent to which it does so through the social mechanism (via higher-order beliefs). To do so, I combine a plausibly natural experiment with a randomized field experiment, conducted in partnership with the UNESCO. Specifically, I analyze the effects of a norms campaign—a media (audio soap-opera) intervention—on a particular set of values and behaviors, namely attitudes and norms surrounding violence against women.

The issue of violence against women is an important and well-suited case for studying the influence of media. First, violence against women is a global concern. It is a violation of human rights and has extensive pernicious consequences that range from the direct physical and mental harm for women and their children to economic losses at the individual and national level. Second, in past years, development programs aimed at improving women’s economic, political, and social status have attracted substantive attention from researchers and policy-makers alike. A particularly popular type of intervention has been media and social norms marketing campaigns, with a special emphasis on “edutainment” (e.g., Paluck and Green Reference Paluck and Green 2009 ). It is crucial to enhance our understanding of the mechanisms behind these policy interventions in order to improve their design and efficacy. Finally, the case of violence against women lends itself for studying the influence of media on social norms as existing evidence points to the link between them. Jensen and Oster ( Reference Jensen and Oster 2009 ) show that the introduction of cable television in India exposed viewers to new information about the outside world and other ways of life, decreasing the reported acceptability of violence toward women. But this effect could also be explained by the publicity of the media, which can plausibly influence social norms via coordination—that is, influencing perceptions of what others think as desirable, and hence promote the rejection of violence because of the expectation that others will reject it as well.

The intervention manipulated the social context in which individuals were able to receive the program. To do so, the research was conducted in San Bartolomé Quialana, a small rural, indigenous community in Oaxaca, Mexico, during May to June 2013, where I combine (1) a plausible natural experiment on the broadcast’s reach with (2) randomly assigned invitations to listen to the program. San Bartolomé Quialana is broadly representative of rural communities, where violence against women is a serious problem (UNESCO 2012 ). With these elements in mind, an audio soap-opera program designed to challenge norms of gender roles and, in particular, discourage violence against women, was broadcasted via the community loudspeaker. This particular loudspeaker had a special characteristic, however. Topography conditions affected its reach, precluding part of the community from accessing the broadcast. This is important because only the area outside the loudspeaker’s reach provides the leverage to test the individual mechanism. Within this area, households were randomly invited to listen to the program, individually and privately, using an audio CD ( Audio CD treatment). Here, individuals were unaware of others listening to the program, precluding common knowledge creation and coordination, thus isolating the individual effect. On the other hand, the area within the loudspeaker’s reach allows us to test the social mechanism. In this area, the program was broadcasted once such that households were able to listen to it ( Village Loudspeaker treatment). In addition, households were randomly invited to listen to the program at a community meeting type of set-up. That is, they were invited to listen to the same program at the same time, but to do it physically copresent with other members of the community ( Community Meeting treatment). This treatment might facilitate the generation of common knowledge and, importantly, aims to match the invitation component of the Audio CD treatment. Overall, the design created four groups as shown in Table 1 .

Table 1 Groups Created by the Research Design

Measuring norms, attitudes, and behavior with a survey of 340 individuals in 200 households, I find that media influence is driven by social effects rather than individual persuasion. I also find that social interactions such as community meetings are not always necessary conditions for such effects. The evidence suggests that the social channel decreased personal and perceived social acceptance of violence against women and increased support for gender equality roles while also increasing pessimism on whether violence will decline in the future. In contrast, results show that the individual channel had no effect.

A competing explanation is that systematic differences may exist between the areas within and outside the loudspeaker’s reach, which could potentially affect beliefs and behaviors related to violence against women. I argue that this is not the case and show that a battery of individual and household characteristics are balanced between the two areas. Given the small size of the town and the nature of the treatment conditions, another concern is that the design could have been vulnerable to spill-overs. However, as I further discuss below, the experiment was designed to address this issue to the greatest extent possible, and most importantly, the presence of spill-overs would bias against the findings of the paper.

This study joins the growing literature demonstrating that exposure to information provided by mass media can influence a wide range of attitudes and behaviors. This paper contributes to this literature by empirically distinguishing the individual and social effects of media influence. This is important for several reasons. First, it improves our understanding of the mechanisms via which media impacts attitudes and social norms; these estimates help resolve an extant puzzle in the empirical literature on media influence. Second, such estimates are critical for thinking about questions of policy interventions. Third, it also sheds light on the way media interventions may have pernicious or unintended effects.

Norms are important because they are standards of behavior that are based on widely shared beliefs of how individual group members ought to behave in a given situation. As such, these customary rules of behavior coordinate individuals’ interactions with others (Young Reference Young 2008 ), and because of this, they are highly influential in shaping individual behavior, including discrimination and violence against a specific group, such as women. Norms can protect against violence, but they can also support and encourage the use of it. For instance, acceptance of violence is a risk factor for all types of interpersonal violence (Krug et al. Reference Krug, Dahberg, Mercy, Zwi and Lozano 2002 ). Indeed, behavior and attitudes related to violence toward women are shaped and reinforced by social norms in general, and gender stereotypes and expectations within the society in particular. These norms persist within society because of individuals’ preference to conform, given the expectation that others will also conform (Lewis Reference Lewis 1969 ; Mackie Reference Mackie 1996 ). That is, participation in such norms and behaviors (or the diffusion of new ones) is a coordination problem. This is because people are motivated to coordinate with one another when there are strategic complementarities: Social approval is only accrued by an individual if a sufficient number of people express their attitudes and behave in a similar way. Conversely, social sanctions can be inflicted on those with different expressed attitudes and behaviors if others do not join them (Coleman Reference Coleman 1990 ). For instance, these sanctions can take the form of shaming, shunning, or any other form of social ostracizing (Paluck and Ball Reference Paluck and Ball 2010 ). Other scholars argue that norms are self-sustaining irrespective of the threat of punishment. Two other mechanisms sustaining norms are (i) negative emotions such as guilt or shame that are triggered when norms have been internalized and (ii) the desire to avoid intrinsic costs that would result from coordination failure (Young Reference Young 2008 ). In short, beliefs about the acceptability of a given behavior, such as violence against women, are a key factor in explaining their occurrence (Mackie Reference Mackie 1996 ).

One might object that violence against women might be driven by different forces as it is often a private interaction in the household, and presumably people will not engage in violent acts simply because they think others do. But a person engaging in violence might often think about the overall social context. For instance, whether people who find out about these actions will understand it as a crime, and report it. Bancroft makes this point when discussing the psychology of abusive men as follows: “While a man is on an abusive rampage, verbally or physically, his mind maintains awareness of a number of questions: ‘Am I doing something that other people could find out about, so it could make me look bad? Am I doing anything that could get me in legal trouble?’” ( Reference Bancroft 2003 : 34). Furthermore, even if the physical consequences of domestic violence can be hide publicly, other behaviors surrounding gender inequality, such as early marriage or lack of financial independence, are more visible.

Because of these considerations, numerous policies and programs have embarked on ambitious campaigns to address social issues like violence against women by promoting changes in social norms. Many of these strategies take the form of media-driven information interventions, such as TV or radio soap operas (Paluck and Ball Reference Paluck and Ball 2010 ). These efforts raise fundamental questions about the extent to and the conditions under which media can influence social norms in general, and about the microfoundations of such process in particular. Media influence can be broadly decomposed into two effects: (1) an individual or direct effect, and (2) a social or indirect effect.

Individual Effect

The individual or direct effect of media relies on persuasion . The emphasis is on the persuasive power of the content, which ignites an individual learning process, updating personal values and beliefs (Staub and Pearlman Reference Staub and Pearlman 2009 ; DellaVigna and Gentzkow Reference DellaVigna and Gentzkow 2010 ). This “individual educational process” is in line with arguments put forward by social learning theory, where the educational effect of media works via educational role models (Bandura Reference Bandura 1986 ). These educational role models are able to perform an instructive function, and transmit knowledge, values and behaviors among others.

Social Effect

Media can also have an effect via a social mechanism . Here, media influence is rooted in the fact that it can provide information in a way that enhances coordination on a norm or action through the creation of common knowledge (Chwe Reference Chwe 2001 ) This is because media’s method of delivery is a public one. Information that is known to be publicly available helps individuals to form an understanding of their shared beliefs. Public information not only causes individuals to update their personal beliefs, but also allows them to update their beliefs about how widely these beliefs are shared (Morris and Shin Reference Morris and Shin 2002 ). That is, public information is used to know that others received the information, and that everyone who received the information knows that everybody else that received the information knows this, and so on, creating common knowledge. In this vein, some authors argue that “attempts to change public behaviors by changing private attitudes will not be effective unless some effort is also made to bridge the boundary between the public and the private” (Miller, Monin and Prentice Reference Miller, Monin and Prentice 2000 : 113).

Moreover, a social effect might be present even in the absence of an individual effect. That is, people might adjust their behavior and publicly expressed attitudes, but not necessarily their private beliefs. Such inconsistency between private and public is known as pluralistic ignorance , which describes situations in which most members of a group privately reject group norms, yet they believe that most members accept them (Miller and McFarland Reference Miller and McFarland 1987 ). Such erroneous social inference facilitates a social effect in the absence of an individual effect.

Consequently, I argue that the method of dissemination is a significant driver of individuals’ beliefs (and higher-order beliefs), and consequently, of their behavior. A public transmission of information—vis-à-vis a private one—facilitates the creation of common knowledge, thus increasing its influence on social norms. Footnote 1 This is the main hypothesis of this paper:

Hypothesis 1: (Common Knowledge). The effect of information on attitudes and norms is greater when the method of delivery is public.

A public method of dissemination helps bring about, but by no means guarantees, common knowledge, and coordinated action (Chwe Reference Chwe 1998 ). Individuals might not know with certainty that others received the information, and thus everyone who received such information might not know with certainty that everybody else that received the information knows that others received the information, and so on. That is, a public promotion may nonetheless be affected by uncertainty surrounding higher-order beliefs. However, this uncertainty is influenced by the type of social interactions created by the conditions under which norms’ promotion is received. In particular, certainty can be bolstered through face-to-face interactions, such as community meetings (Mackie Reference Mackie 1996 ; Chwe Reference Chwe 2001 ).

To address this heterogeneity within the public dissemination of information, I explore the extent to which different levels of uncertainty and potential social interactions moderate the diffusion of norms. Within the common knowledge framework, I analyze whether the publicness of the information is a sufficient condition for media influence and whether face-to-face interactions enhances such influence. That is, I disaggregate Hypothesis 1 into two secondary hypothesis:

Hypothesis 2a: (Public Signal). A public method of delivery is a necessary and sufficient condition for information to influence attitudes and norms (i.e., no social interaction is required).

Hypothesis 2b: (Face-to-Face). A public method of delivery of information with face-to-face interactions enhances the effect of information on attitudes and norms.

To test these hypotheses, I conducted a media intervention in San Bartolomé Quialana, in partnership with the UNESCO Office in Mexico. San Bartolomé Quialana (or simply Quialana) is a small rural, indigenous community located in the state of Oaxaca. Its key features are broadly characteristic of rural municipalities in the rest of Mexico. (Section A1 provides further details.) For the purposes of this paper, an important aspect of Quialana is its cultural homogeneity. For example, as of 2010, out of the 2470 habitants, 2412 were born, and raised in Quialana. This is important because the ability to focus on a single community, holding cultural, and social aspects “constant,” makes it easier to isolate the individual-level informational mechanisms that drive media influence on attitudes and social norms.

The Soap-Opera

The intervention consisted of an audio soap-opera designed to challenge gender role norms and discourage violence against women. Entitled Un nuevo amanecer en Quialana ( A new dawn in Quialana ) it was produced in conjunction with a regional partner non-governmental organization (NGO) and it included four episodes of ~15 minutes each, for a total running time of 57 minutes. The soap-opera was embedded in the local context, featuring common reference points such as “Tlacolula’s market,” as helping the audience to directly relate to the situations portrayed can increase its effect (La Ferrara, Chong and Duryea Reference La Ferrara, Chong and Duryea 2012 ). The plot evolved around a young couple who fell in love and started a family in Quialana. The narrative was developed such that the leading male character gradually transformed from a loving and caring husband to a violent and aggressive figure. Research shows that the male figure should not be displayed as a completely violent character from the outset so that listeners can create a rapport with him and not disregard his behavior as an exception (Singhal et al. Reference Singhal, Cody, Rogers and Sabido 2003 ). Moreover, the language of the script used injunctive norms (Paluck and Ball Reference Paluck and Ball 2010 ). For instance, instead of arguing “beating women is wrong” the soap-opera would say “citizens of Quialana believe that beating women is wrong.” This actually biases against the main hypothesis of this paper because those in the Audio CD treatment are exposed to these injunctive norms. One caveat of the narrative, however, is that it did not contain channel factors to act out these norms. Footnote 2

Un nuevo amanecer en Quialana was broadcasted using the community loudspeaker as a special event: the premier of the first-ever locally produced soap-opera, and the first time the community loudspeaker was used for entertainment purposes.

The research design combines two sources of variation. Specifically, the social context in which people are able to receive the intervention is manipulated by (1) exploiting arguably exogenous variation generated by the topography of the community (i.e., within community variation of “broadcast access”), and (2) randomly inviting households to listen to the program. I further describe each one below.

Natural Experiment: Loudspeaker, Topography, and Sound Check

While Quialana did not have a local radio at the time of the intervention, it did posses a loudspeaker—located on top of the Town Hall, in the center of the community. Before the intervention, the loudspeaker primarily and only sporadically announced sales of small-scale household goods, such as construction materials, like bricks, or other livestock. It was never used for other announcements like news, weather, etc. Perhaps for these reasons the variation in the loudspeaker’s reach (and it’s sharpness) described below came as a surprise to many of our local partners who had previously taken for granted that nearly everyone in the community had access to the occasional announcements.

Leveraging variation in the loudspeaker’s reach, I define two areas within Quialana: (1) the area within the loudspeaker’s reach , and (2) the area outside the loudspeaker’s reach . This within community variation is mainly a product of topography conditions. For example, from one end of the municipality to the other there is an altitude difference of more than 500 ft. More specifically, in some areas, the slopes become high enough that they preclude the sound to travel with clarity. Footnote 3 That is, the source of variation is not a function of distance to the loudspeaker per se , but mainly of altitude difference. That is, two households can be located at the same distance from the loudspeaker and still one of them can fall within the loudspeaker’s reach and not the other. Figure 1 shows the loudspeaker’s reach, which was determined via a sound-check process from the ground (further explained in Section A2).

Fig. 1 San Bartolome Quialana and its loudspeaker’s reach Note : Population (green), households (brown). Red line: loudspeaker’s reach. Red filled circle: Loudspeaker.

A valid concern is that systematic differences may exist between these two areas, which could potentially be correlated with attitudes and norms related to violence against women. One of the advantages of conducting the study within a single, small (slightly more than a mile long) community is precisely being able to leverage the cultural homogeneity and ameliorate concerns about such potential differences. Based on informal and formal discussions with UNESCO personnel, NGO partners, and citizens of Quialana there is no qualitative evidence of sorting into one area or another based on attitudes and behavior related to gender inequality. Qualitative analyses and focus groups organized by UNESCO suggested that violence was widespread equally across the community (UNESCO 2012 ). I complement these on-the-ground accounts with quantitative evidence. Specifically, I rely on data from the 2012 National Housing Inventory to show that a battery of individual and household characteristics are balanced between the two areas (Table A2).

While the focus on a very small community, alongside the qualitative and quantitative evidence strengthens the plausibility of the natural experiment, such interpretation might be threatened if unobservables linked to each of the two areas are also linked with attitudes and behaviors towards women. This should be taken into account when interpreting the results.

Randomization: Audio CD and Community Meeting

Within each area, households were randomly invited to listen to the soap-opera via systematic sampling with a random start, creating the Community Meeting and Audio CD treatments. Here, the experiment was able to hold the content of the media program constant while varying the social context in which it was received. In the area within the loudspeaker’s reach, households were invited to listen to the program in the cafeteria next to the Municipal building (i.e., Community Meeting ). In the area outside the loudspeaker’s reach, households were invited to listen to it in their homes using an audio CD (i.e., Audio CD treatment). The regional partner NGO served as the public face of the treatments, presented as part of an initiative to create a local radio station and as such, there was no mention of UNESCO’s involvement.

To test the individual mechanism, the invitation to listen to the soap-opera (via the audio CD) had to be privately delivered to the household. Here, caution was taken to prevent households from believing that other households were also receiving the program—although as argued before, this would bias against my hypotheses. Enumerators were trained to keep away from sight any material that would signal that other households were also being approached. Further, when reaching out to the household, enumerators emphasized that the audio CD was a pilot program, arguably a one-time opportunity to preview it and provide feedback. While not explicitly saying that the household was the only one selected to receive the audio (to avoid deception), enumerators were trained to hint at that possibility and to frame such opportunity as something very novel, exclusive and private—which might explain the perfect level of compliance. As such, audio CDs were handed out along with a short questionnaire meant as a listening-check device: the enumerator would leave the audio CD and questionnaire sheet and then stop by a couple of hours later to pick up the sheet, and based on this, compliance was 100 percent Footnote 4 . Because of this set-up and based on comments from enumerators, in some cases all family members were present at the time and reportedly all listened to it, but in other cases not every household member was present at the time, and hence did not listen to it.

To test the social mechanism, the design created a comparable treatment group, the Community Meeting , where the invitation to listen the soap-opera matches the invitation component of the Audio CD treatment. Moreover, the Community Meeting provides leverage to explore the effects of public information. By creating a very particular form of social interaction (or at least the knowledge about it), namely the community meeting, this treatment might increase the level of certainty individuals’ have about others receiving the information, and so on. At the same time, this common knowledge mechanism might be confounded by other potential interactions facilitated by the meeting, such as deliberation. To be clear, during the community meeting there was no deliberation (out of respect to other listeners, conversations were not allowed). However, deliberation and exchange of opinions could have occurred after the meeting. Inasmuch these interactions are indeed facilitated by the creation of common knowledge, the design is able to disentangle the social and individual mechanisms of media influence (however, it cannot unbundle face-to-face certainty from deliberation). Finally, people from roughly one in four households invited to the Community Meeting actually went to the cafeteria—that is, complied with the Community Meeting treatment. Anecdotally, during the broadcast people did stop by the Town Hall, just outside the cafeteria where the community meeting was taking place, and listen to the soap-opera (or a least parts of it) from just outside. Other accounts point to the fact that many simply listened to the soap-opera from their own houses.

However, to fully understand the social mechanism, I explore whether the public transmission of information is a sufficient condition to influence norms as well the extent to which the face-to-face interactions can enhance the effect on norms. To potentially address this, the design created a public treatment without imposing such social interactions: households who were able to listen to the broadcast by being within the loudspeaker’s reach but were not in the Group condition constitute the Village Loudspeaker treatment.

Finally, households outside the loudspeaker’s reach who did not receive the audio CD represent the baseline group . These four conditions are summarized in Table 1 .

An unbiased estimation of the mechanisms relies on two dimensions: one, facilitating the creation of common knowledge in the social conditions, and two, precluding it in the individual condition (i.e., no spill-overs). First, for the broadcast to facilitate the creation of common knowledge, it should be the case that people who listens to it know that other people are hearing it too. This is less of a concern in the Community Meeting treatment because information is explicitly given to the household, so they know that others are also receiving the invitation, and so on. However, a person in the Village Loudspeaker treatment might believe that she has heard the broadcast, say because she lives close to the Town Hall or because she believes she has particularly good hearing but that few of her neighbors actually have heard it. I attempt to address this in two ways. First, I include distance to the Town Hall as a control covariate in the empirical analysis. This variable is also a relevant covariate inasmuch it also works as a proxy for population density, which might be a potential confounder with respect to the perpetration of violence. Second, as discussed below, the empirical strategy relies on the estimation of intention-to-treat effects (ITT) precisely because individuals might fail to comply with the treatment—in the case of the Village Loudspeaker , individuals might not listen to the program nor realizing that others are listening to it as well, and so on. As such, it represents a conservative or lower bound estimation.

The second dimension is linked to the notion that those who receive the individual treatment should be unaware of other treatments. Given the small size of the town and the nature the treatment conditions, the design was vulnerable to spill-overs. However, such spill-overs would bias against the main hypothesis of the paper. This is because those in the individual condition might find out that other people were also receiving the soap-opera. Nevertheless, in order to minimize potential spill-overs, invitations for the Community Meeting were given out on a Friday. Both treatments were administered the next day: the Audio CD treatment was conducted on Saturday—starting early in the morning, and the Village Loudspeaker and Community Meeting broadcast was also implemented on Saturday, during the evening.

Similarly, the design faced a trade-off between minimizing these spill-over concerns and maximizing the intensity of the treatment. For the former, the ideal was to minimize the time between the treatments and the survey. For the latter, an alternative was to implement a weekly soap-opera over several weeks or months. Given that the main goal of this study was to analyze the underlying mechanisms of media influence, I prioritized addressing the spill-over concerns at the expense of a limited intensity of the treatment. Nonetheless, experiments where only one day or even 1-hour interventions were implemented have found profound effects (e.g., Ravallion et al. Reference Ravallion, Walle, Dutta and Murgai 2015 ). Given these considerations, the norm intervention was implemented as a one-day event only, and the surveys were administered over the following few days.

Outcome Measurement

The regional partner NGO also served as the public face of the survey, presented as a mean to retrieve the opinion of Quialana citizens to inform an initiative for starting a community radio. Footnote 5 In the survey, three questions measured respondents’ beliefs and estimation of others’ beliefs and actions with respect to violence against women, and three other questions measured attitudes and individual actions related to it. Hence, I evaluate six outcomes of interest, which I describe in detail below.

The first dependent variable is a measure of Personal beliefs aimed at capturing the extent to which people believe and are willing to state that violence against women is a recurring problem in the community. The question asked was “Do you think that violence against women is something that happens here in Quialana?” and it was coded from 1 (“No, this never happens here in Quialana”) to 5 (“This happens too much in Quialana”). Given the qualitative evidence that violence is pervasive in Quialana (UNESCO 2012 ). This item was designed not to capture such factual scenario, but instead the respondent’s personal beliefs about the desirability of (and hence, willingness to expose) certain actions. In other words, the intuition behind this question is to capture the shift from a perception where “husbands are never violent to their wives—they might engage in some aggressive behavior but that is not violence” to a situation in which “that” type of behavior is recognized as violence, and moreover, it is socially appropriate to judge it as serious problem.

The second variable of interest captures the Perceived social rejection . That is, the extent to which an individual believes that the community believes violence is a problem. The question was “Do you think that that the community, the neighbors, and other families see violence against women as a serious problem here in Quialana?” with responses coded from 1 (“No, they do not see it as a problem at all”) to 4 (“They see it as a serious problem that needs to change”). As in the previous question, this item aims to measure the shift in norm perception from a norm where violence is tolerated (e.g., the community experiences violence but sees it a routine and excusable) to a norm where violence is rejected.

The third variable, Expectations about the future , measures individual expectations that this type of violence will decline in the future. The question was “Do you think the next generation of Quialana males …?” with answers being coded from 1 (“Will abuse women more”) to 4 (“Will never abuse women”). That is, higher values represent more optimistic views about the future.

While these three measures are able to retrieve individuals’ perception about norms surrounding violence against women, they do not directly measure individual attitudes, beliefs, nor actions regarding gender roles or domestic violence. Outcomes four through six address this, including a behavioral outcome embedded in the survey.

The fourth outcome, Value Transmission , measures the extent to which the respondent would educate a child with gender equality values. This captures the parents’ decisions concerning which values to inculcate in their children, which are affected by perceived prevailing values in the society (Tabellini Reference Tabellini 2008 ). In particular, it focuses on attitudes toward equality regarding household chores, which is seen by many as one of the key challenges for achieving gender equality (World Bank 2012 ). The question was “Would you educate your child so that domestic chores, such as doing laundry and cooking, are as much a responsibility of the men as they are of the women?,” with the answer being coded 1 if the respondent supports this type of education, 0 otherwise.

The fifth variable captures the individual Reaction to an episode of violence . The question was “If you see or hear a neighbor’s wife being beaten by her husband, what would you do?.” Responses are collapsed into a binary variable in the following way: Reaction to violence takes a value of 1 if the respondents answers that they would interrupt the couple so to stop the violence and/or call the police so they intervene, and is coded 0 if the answer implies that they would not take any action at the moment. Footnote 6

The sixth variable retrieves a behavioral outcome. Survey respondents were asked if they would sign a petition to support the creation of a violence against women support group: the variable Petition signature is coded 1 if they signed the petition, 0 otherwise.

To account for multiple testing I also analyze an Index variable created using standardized inverse-covariance-weighted (ICW) averages of the previous variables. The scale of the resulting index is in control group standard deviations, and higher values can be interpreted as higher levels of rejection and perceived rejection of violence against women and increased support for gender equality.

Three key covariates were collected, namely gender, age, and education. A total of 200 households were surveyed; this represents about one in every three households in Quialana. When available, both the male and female heads of the households were surveyed. This generated a maximum of 340 observations. Table A8 shows descriptive statistics and Section A4 shows randomization checks.

The empirical strategy relies on estimating ITT effects. In this particular set-up, however, the invitation to the Community Meeting (i.e., the assignment to treatment) matches the theoretical motivation behind the treatment itself. That is, the invitation provides specific information about how the soap-opera is going to be disseminated (i.e., there will be a broadcast and an event where people are able to receive the program together), thus facilitating the creation of common knowledge. This also has implications for estimating local average treatment effects (LATE) as it may be read as a violation of the exclusion restriction—this precludes an unbiased estimation of the LATE, providing further reasons to focusing on the ITT estimation.

I conduct the analysis using ordinary least squares, with two empirical strategies, namely (1) Community Meeting versus Audio CD and (2) all four treatment conditions. Footnote 7

Social and Individual Mechanisms: Community Meeting Versus Audio CD

The first empirical strategy focuses on testing the Community Meeting and Audio CD treatments against each other, as follows:

The coefficient of interest in Equation 1 is α ; it captures the social mechanism underlying norms diffusion. Hypothesis 1 predicts α >0. Nonetheless, I test it with a two-sided test.

All Treatment Conditions: Full Sample

The estimates of the Community Meeting are able to isolate the social effects induced by common knowledge. However, they might be influenced by the increased certainty created by the face-to-face interaction, and might potentially be confounded by other social interactions—facilitated by the community meeting—such as deliberation. To address this and understand the extent to which a public method of delivery is a sufficient condition to influence norms, I rely on the full sample, as follows:

where Y i , h represents the outcomes (continuous variables are expressed in standard deviations of the distribution of responses in the baseline condition). The vector of controls, Community Meeting and Audio CD are defined as before. VillageLoudspeaker is an indicator for whether a household is within the loudspeaker’s reach but was not invited to the meeting. Finally, those living in the individual area without treatment represent the baseline category .

In Equation 2, the coefficients of interest are α , β , and γ . They measure the effect of the intervention and, by design, can shed light on the different potential mechanisms. Here, Hypothesis 1 predicts α > β and γ > β , and more specifically, Hypothesis 2a predicts γ >0 while Hypothesis 2b predicts α >0 with α > γ .

Community Meeting Versus Audio CD

Table 2 displays the results for each outcome of interest using two different specifications. The first one displays a specification using only the Community Meeting indicator (i.e., α ), while the second one includes the vector of control covariates.

Table 2 Community Meeting Versus Audio CD

Note : Standard errors clustered at the household level in parentheses.

Covariates: age, female, education, distance.

ICW=inverse-covariance-weighted.

+ p<0.10, *p<0.05, **p<0.01.

Results regarding to the influence on personal beliefs suggest that those invited to the community meeting were more likely than those invited to the Audio CD to state that violence against women is a recurring problem in Quialana. The parameter estimate gains precision when introducing controls but remains stable ranging from 0.33 to 0.35 SD relative to the Audio CD condition (p-value=0.065 and p-value=0.052, respectively).

When looking at the perceived social rejection, the evidence points in the same direction, with stable (0.66 and 0.65) and precise estimates.

The community meeting effects on expectations about the future are negative, very stable (−0.48 and −0.42) and statistically significant at conventional levels, suggesting that those invited to the meeting became more pessimistic about the decrease of violence in the future. This arguably perverse effect could be explained by several factors. One explanation might be that, while the community meeting induced coordination around a new injunctive norm (i.e., people in Quialana should reject violence) it also raised awareness and facilitated coordination around a more subtle descriptive norm, namely that violent behavior is prevalent in the community. This more precise belief about the current situation of the community, coupled with the fact that the soap-opera did not offer any channel factors to act upon it, might have induced pessimistic expectations for the future extent of violence. Another explanation is that, as a result of the new common knowledge, individuals may foresee an increase opposition to violence against women, which in turn may potentially lead to a backlash effect. For instance, more women may speak out and oppose violence, creating a more violent response from a subset of men. While the data does not allow me rule out or pin down a particular explanation, it nonetheless shows that this effect is driven by a social mechanism.

The analyses of individual actions also support the social mechanism. Those invited to the community meeting were 16 percentage points more likely (Model 8) than those invited to the Audio CD to say they would educate their children on gender equality values, 20 percentage points more likely to react to a violent event (Model 10), and 20 percentage points more likely to sign the petition (Model 12).

The ICW Index analysis confirm these results. Subjects invited to the community meeting have an index of responses 0.45 SD higher than those invited to the Audio CD.

To address concerns about the plausibility of the natural experiment, Table A11 replicates the analysis restricting the sample to households within 300 m of the Town Hall, finding similar results.

The overall evidence is clear. Media influence, captured by changes in beliefs, attitudes, and behavior, is primarily driven by a social channel. However, creating common knowledge might also facilitate a more precise belief of the status quo, thus setting negative expectations about future change, as suggested by the evidence on beliefs about the future prevalence of violence.

All Treatment Conditions

Table 3 displays the results for the full sample, without and with controls.

Table 3 All treatment conditions.

The analyses on personal and perceived social rejection show that the informational effects on beliefs and norms are driven entirely by the social mechanisms. When analyzing the expectations about the future, the estimated parameters for social treatments are similar in size, ranging from 0.20 to 0.24, and once again showing a negative sign. In contrast, the Audio CD parameters are positive but far from statistically significant.

These first set of results support both the community meeting and Village Loudspeaker treatments. While the analyses of individual attitudes and actions also support the social mechanism, the evidence is stronger for the community meeting—supporting Hypothesis 2b. A similar pattern emerges when analyzing the ICW Index.

Additionally, I estimated several F -test of inequality of coefficients. When comparing either one of the social conditions to the Audio CD ( β ), they tend to show a statistically significant difference at conventional levels, supporting Hypothesis 1. When pushing further the analysis of the social mechanism, the evidence shows that publicness in and of itself can be a sufficient condition to diffuse norms, in favor of Hypothesis 2a. Nonetheless, some of the evidence also suggests that face-to-face interactions can indeed enhance such effect, providing some support for Hypothesis 2b.

As before, I replicate the analysis by analyzing households within 300 m from the Town Hall, finding the same results (Table A12).

Overall, these findings again suggest that social mechanisms are the main drivers behind media influence on attitudes and norms.

A valid concern when interpreting the results is the extent to which they represent a one-off case in a unique setting. As noted before, in many aspects, Quialana is similar to many other municipalities in Mexico as a community with high levels of media consumption and issues with gender inequality and violence against women. Similarly, as a large and diverse society aiming to empower women so to overcome social challenges, Mexico has much in common with other developing and even developed countries. (See Section A6 for a more detailed discussion.) Yet, to what extent are the results from this study externally valid in the sense that they generalize beyond Quialana? While there are numerous variations in context or treatment design that could change the estimates presented here, the results nonetheless speak to a plausibly phenomenon; the notion that public information, via common knowledge and coordination, can induce differences in norms and behavior is often stated as a general proposition instead of stated as applying to a particular context (Chwe Reference Chwe 2001 ).

Three particular results merit further exploration. First, the negative results on expectations about the future was arguably surprising. Further understanding the conditions under which these type of backlashes occur and can be precluded (e.g., emphasizing channel factors ) is theoretically and policy relevant. Second, the mixed results on the Village Loudspeaker point to the need for more inquiry into the conditions under which public information is a sufficient condition to influence norms and the conditions under which securing common knowledge via social interactions is actually necessary. Third, the Audio CD results suggest that private persuasion in this context was ineffective. From the point of view where social norms are deeply embedded in a community, this result is arguably not surprising precisely because it does not have such link with the community. However, it might also be specific to the issue at hand—perhaps, in less sensitive issue areas, where social pressures might carry relative less weight, individual persuasion might be more effective.

Finally, there are potential concerns about whether the changes in reported attitudes, represent changes in behaviors, or just in reporting. Despite the behavioral evidence on the petition signature , one may be still concerned that public treatments only change what respondents think other people want to hear and see about the acceptability of violence, but does not actually change the incidence of abuse. Without directly observing people in their homes, however, it is difficult to conclusively separate changes in reporting from changes in behavior. However, if media interventions only change what is reported, it still represents social norms change and progress. Changing social norms is a necessary (Jensen and Oster Reference Jensen and Oster 2009 ) and can be sufficient step toward changing the desired outcomes (Mackie Reference Mackie 1996 ).

It is well know that exposure to information provided by the media outlets can influence a wide range of attitudes and behavior. However, less is known about the specific mechanisms behind such influence. Two broad mechanisms can account for such effects, namely an individual mechanism based on persuasion and a social mechanism based on higher-order beliefs and coordination. This paper examines these mechanisms and disentangles their effects at the individual level, studying attitudes, and norms toward violence against women.

The evidence presented here shows a very consistent story: media influence on attitudes and social norms is driven mainly by social effects rather than individual persuasion. First, I show that a public method of delivery was able to decrease personal and perceived social acceptance of violence against women and increased support for gender equality roles, whereas a private delivery had no discernible effects. I also show that public information is no panacea as it also increased pessimism on whether violence will decline in the future. Second, I present evidence that a pure public method of delivery (i.e., without social interactions) can be a necessary and sufficient condition to influence attitudes and norms.

Overall, further understanding the interaction between individual beliefs and different types and sources of information can shed light on the social mechanism purported here.

Eric Arias, Postdoctoral Research Fellow at the Niehaus Center for Globalization and Governance, Princeton University, 432 Robertson Hall, Princeton, NJ 08544 ( [email protected] ). This research was carried out as part of a UNESCO Mexico program. the author especially thanks Samira Nikaein at the UNESCO Office in Mexico, Michael Gilligan and Cyrus Samii for their help and support. The author also thanks Michaël Aklin, Karisa Cloward, Livio Di Lonardo, Pat Egan, Jessica Gottlieb, Macartan Humphreys, Malte Lierl, Sera Linardi, Alan Potter, Peter Rosendorff, Shanker Satyanath, David Stasavage, Scott Tyson, participants at ISPS-Yale, WESSI-NYU Abu Dhabi, APSA, MPSA and PEIO for their suggestions and comments. All errors and interpretations are the author’s alone and do not necessarily represent those of UNESCO. To view supplementary material for this article, please visit https://dx.doi.org/10.1017/psrm.2018.1

1 Arguably, “strong” and “weak” hypotheses can be derived. The strong hypothesis would imply that only by increasing the publicness of the information above a certain threshold one should expect an effect, that is, a “tipping-point” argument (Finnemore and Sikkink Reference Finnemore and Sikkink 1998 ). The weak version would postulate that by increasing publicness one is able to increase the effect. Differentiating between these two is beyond the scope of this paper. See also Gottlieb ( Reference Gottlieb 2015 ).

2 Channel factors are small but critical factors that facilitate or create barriers for behavior, for example, the promotion of a telephone hotline number that provides information and can refer callers to service providers (Singhal et al. Reference Singhal, Cody, Rogers and Sabido 2003 ).

3 For examples, see Figures A2 and A3.

4 Almost all households played the audio CD on their own stereo systems, and when they did not have one, enumerators would offer to lend “their personal” portable CD player. The questionnaire consisted on rating the soap-opera, asking the name of the character with whom they identified the most, and providing space for comments.

5 Surveys were collected at the respondents’ households from June 3 to June 5.

6 Answers that take the value of 1 are of the type “call the police” and/or “interrupt them to stop it,” while answers coded 0 are “do nothing, because it’s a private matter between husband and wife” or “do nothing at the moment, but ask what happened later.”

7 Results using logistic models are substantially the same (see Online Appendix).

Arias supplementary material

Online Appendix

Arias Dataset

This article has been cited by the following publications. This list is generated based on data provided by Crossref .

• Google Scholar

View all Google Scholar citations for this article.

Save article to Kindle

To save this article to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle .

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

• Volume 7, Issue 3
• DOI: https://doi.org/10.1017/psrm.2018.1

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox .

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive .

Reply to: Submit a response

- No HTML tags allowed - Web page URLs will display as text only - Lines and paragraphs break automatically - Attachments, images or tables are not permitted

Your details

Your email address will be used in order to notify you when your comment has been reviewed by the moderator and in case the author(s) of the article or the moderator need to contact you directly.

You have entered the maximum number of contributors

Conflicting interests.

Please list any fees and grants from, employment by, consultancy for, shared ownership in or any close relationship with, at any time over the preceding 36 months, any organisation whose interests may be affected by the publication of the response. Please also list any non-financial associations or interests (personal, professional, political, institutional, religious or other) that a reasonable reader would want to know about in relation to the submitted work. This pertains to all the authors of the piece, their spouses or partners.

The Global Social Media Experiment

An international collaboration testing the causal impact of social media around the globe

Led by Steve Rathje , Nejla Asimovic , Claire Robertson , Tiago Ventura , Joshua Tucker , and Jay Van Bavel

Almost 5 billion people use social media worldwide. While much of research on social media has been conducted in the US and UK, emerging evidence suggests that social media might have very different effects on countries outside the US ( Asimovic et. al, 2021 ; Ghai et. al, 2023 ; Lorenz-Spreen et. al, 2022 ). With social media’s massive global usage, it is crucial to examine the causal effects of social media on important psychological outcomes, such as polarization and well-being.

We plan to conduct a global field experiment across multiple countries to test the causal effect of social media on polarization, intergroup attitudes and well-being around the world. Similar to prior “global studies” conducted with the Social Identity and Morality Lab , such as the International Collaboration on Social and Moral Psychology: Covid-19 and the International Collaboration to Understand Climate Action , we aim to collaborate with a large team of researchers from countries around the globe to conduct a cross-cultural field experiment. In this global field experiment, participants will be incentivized to temporarily reduce their social media screen-time for ~2 weeks. We will then examine how reducing social media usage affects polarization, intergroup attitudes, well-being, and a number of related outcomes (e.g., trust, political participation, belief in misinformation, etc.). The methods of the study will be modeled after prior social media deactivation studies (e.g,  Asimovic et. al, 2021 ;  Alcott et. al, 2020 ). We will also examine whether the effects of social media cessation are moderated by a number of country-level variables (such as the strength of a country’s democracy, etc.) and individual difference variables. This global study will help inform a number of debates about the effect of social media in different cultural and political contexts. On March 27, 2023, we sent out a call for collaborators to help us conduct this global field experiment. Over 800 researchers residing in 76 countries have filled out this call for collaborators survey, and reported being able to assist with collecting data in 103 countries (shown in green in the below map). This project is funded by the National Science Foundation , the Templeton World Charity Foundation , and a “seed grant” from New York University.

The above map shows the countries that collaborators have reported being able to collect data from. Over 700 researchers residing in 76 countries report being able to collect data from 103 total countries (shown in green).

• Copy shortlink
• Report this content
• Manage subscriptions

Need a hand creating engaging content? Try Buffer for free →

A Simple 6-Step Framework for Running Social Media Experiments (with 87 Ideas Included)

Experiment with ideas. Test and see which works better. Analyze your data.

These are phrases we often use on this blog. To us, social media marketing is a bit of a science . We recommend testing things, running experiments, and analyzing data — because it worked for us. This experimental mindset has helped us grow our social media results .

But one thing we haven’t done well is to explain the how: how to run social media experiments .

In this post, you’ll learn the six simple steps of running social media experiments. We’ve even included 87 ideas, which you can start testing immediately.

How to run social media experiments successfully

Running social media experiments can be hard when you’re not sure where to start and where to head to. Here’s a step-by-step guide to help you hit the ground running.

Before we dive into the guide, here’s a quick caveat: social media experiments are not perfect or entirely scientific . Some factors are out of our control, such as organic reach since it’s determined by the social media algorithms .

This doesn’t mean we wouldn’t get meaningful results (it has worked for us and many others); it’s just good to be mindful of this while running your experiments.

1. Set goals

As with most planning, it’s crucial to start by setting your goals. Why?

Imagine the following situation. Both social media posts are sharing the same blog post with a different headline.

Post A received 100 Likes, 100 shares, 10 clicks, and 5,000 impressions.

Post B received 10 Likes, 10 shares, 100 clicks, and 1,000 impressions.

Which post do you think is better?

I think it depends on your goals! If you think social media is for engagement , you’ll likely prefer Post A. But if you think social media is for driving traffic, you’ll probably prefer Post B instead.

Here’s a list of social media goals you could choose from:

• Reach (or impressions)
• Engagement (Likes, comments, and shares)
• Traffic from social media
• Leads from social media
• Revenue from social media

For us, our overarching goal for social media is engagement and brand-building. ( Here’s why .) So we focus more on our social media reach, engagement, and following than traffic, leads, or revenue from social media.

Having said that, each social media post can sometimes have its own micro-goal. For example, while our overall social media goal is engagement and majority of our posts are meant for generating engagement, we have some posts that are meant for driving traffic, such as this and this .

2. Brainstorm ideas

Once you have set your goals, you are ready to come up with ideas. While you are thinking of new ideas, it’ll be good if you could form a hypothesis around the idea, too. This is the format we like to use7

If we  (experiment idea), then (expected results), because (assumptions).

If we curate top content from other Facebook Pages, then we can grow our Facebook reach by 10%, because they are content proven to be popular.

You could also keep it as simple as “Curating top third-party content will increase our reach on Facebook.”

Here are a few suggestions for coming up with social media experiment ideas:

Read blog posts for ideas

This is my favorite method because there’s so much written about social media marketing every day. Listicles and case studies of successful social media tactics can be a great source of inspiration for experiment ideas.

If you want somewhere to get started, we have quite a few blog posts with experiment ideas in them:

• Get Over Your Creativity Block With These 20 Social Media Content Ideas
• 20 Creative Ways to Use Social Media for Storytelling
• 7 Powerful Social Media Experiments That Grew Our Traffic by 241% in 8 Months
• 10 Unique Ideas to Test on Every Social Media Channel (And How to Tell What Works)
• We Made These 10 Social Media Mistakes so You Don’t Have To

There’s also a huge list of 100 social media experiment ideas below. Click here to skip right to it , and feel free to take any of the ideas.

Follow social media trends

The second method is to follow social media trends .

For example, videos are becoming the most popular content format on social media. Facebook has been pushing for videos on its platform for the last few years, and LinkedIn has recently introduced native videos. Our internal data also showed that videos received an average of 873 interactions per post , compared with 279 for photos and 190 for text posts.

So it’ll be a good idea to test videos on your social media profiles .

We recently wrote about the 10 major social media trends for 2018 , which you might find useful for generating ideas.

What ideas can you think of in light of these trends?

Study industry leaders and competitors

The final method is to watch and learn from the best companies in your industry and your competitors. What have they been doing that is worth trying yourself?

It’s also good to be aware that the ideas that worked for them might not always work for you. You all likely have many differences such as branding, positioning, and audience. But if you think an idea is suitable for your brand, I would say go for it and modify it for your own brand.

There are some free tools you can use to track industry leaders and competitors.

On Facebook, you have Pages to Watch . It allows you to quickly check out the recent top posts of similar Facebook Pages. You can find it at the bottom of the Overview tab in your Page Insights .

On Twitter, you could add your favorite companies to a Twitter list . To create a Twitter list, click on your profile photo in the upper-right corner and click on “Lists”. Then, click on “Create new list” and fill out the information.

On LinkedIn, you have the Companies to track feature in your Company Page analytics. Clicking on any of the company names will bring you to their Company Page. You can access this section by clicking on Analytics > Followers.

3. Prioritize

The next step is to prioritize your ideas. A prioritization framework we like to use at Buffer is the ICE score by GrowthHackers.

ICE stands for Impact, Confidence, and Ease.

• Impact: The possible impact of the idea on your selected metric (e.g. 10 percent increase in reach)
• Confidence: Your confidence level about the success of the experiment (e.g. three companies have found success with this idea)
• Ease: The number of resources required (e.g. no design or engineering help needed)

For each experiment, give each factor a score from one to 10. The overall score is determined by taking the average of the three scores. You should start with the experiment that has the highest score.

Here are two simple examples of ICE scoring in action:

Experiment A Curating top third-party content will increase our reach on Facebook. Impact = 6 Confidence = 8 Ease = 8 Overall = 7.3

Experiment B Partnering with micro-influencers will grow our Instagram reach. Impact = 8 Confidence = 4 Ease = 3 Overall = 5

Based on the ICE score, I would run experiment A before experiment B.

While this process can be a little time-consuming at the start, it’s important. It’ll help you think through the experiment details (such as what metric to track) and maximize your impact with the resources you have.

After a while, you should be able to build up a good intuition about the potential of ideas without having to score every single idea.

Now you’re ready to test your top ideas!

There are a few things you want to be mindful when testing.

(Ideally) test one thing at a time  to understand what’s making the difference. For example, if you want to test your copy as an experiment, it’ll be best to keep the multimedia the same. Otherwise, you won’t know if the copy or the multimedia caused one post to outperform the other.

Look at the right metrics  to measure the results of your experiment. This is where the goals you’ve set will be helpful. For instance, if you want to maximize your social media reach, you would pick reach or impressions over clicks.

Run one experiment at a time  for a start. Similar to the first point above, doing so lets you know which experiment moved the needle. (When you are feeling advanced, you could run multiple experiments concurrently as long as you understand how they will affect the metrics.)

Run each experiment for at least a week  for smaller experiments. This isn’t entirely scientific but I believe a week is sufficient for the results to be seen. For bigger experiments such as shifting your social strategy to posting more videos, you might want to test it for a month to a quarter. The bigger the experiment, the longer it should be tested.

5. Analyze and learn

Finally, you’ll want to analyze your results to see if your experiment has been a success. Here are some questions you could ask yourself:

• Did it achieve the results I had expected? Why?
• Did any other factors contribute to the success or failure?
• Can I learn anything else from this experiment?

To help you with your experiment tracking, I’ve created a simple tracking template: Social Media Experiment Tracking . Feel free to make a copy and modify it to your liking.

When running scientific experiments, it’s important to look at the statistical significance of the results — to ensure that the result isn’t a fluke and can be repeated successfully. But for social media experiments, it might not always be feasible. That’s because your sample size (impressions of a post) isn’t within your control.

My non-scientific recommendation here is to repeat the experiment a few times and see if the result remains the same . If the result can be repeated, you can consider turning the experiment into a regular part of your social media marketing.

Congratulations! You have just planned, run, and analyze a social media experiment!

Whether you had a successful experiment or not, it’ll be great to repeat step four (test) and five (analyze and learn) continuously. Then once a quarter, you could take a step back and look at the bigger picture again . Your social media goals might have changed, or there might be new social media tactics to try.

Re-evaluate your goals, brainstorm new ideas, and test them. All the best!

87 social media experiment ideas

To help you get started with running social media experiments, here’s a mega list of ideas for you to try. Some are low-hanging fruits while others might require much effort:

Posting time

• Post when your followers are online
• Post when your followers are offline
• Post during commute times
• Post during lunch time
• Post on the weekends

Posting frequency

Headlines and copy.

• Write short headlines
• Write long headlines
• Write really long headlines (or stories )
• Use social proof in your copy
• Customize your post for each social media platform
• Post questions
• Ask for opinions on a trending topic
• Share top industry news
• Share thought-leadership articles
• Share interesting, relevant statistics
• Share inspiration quotes
• Post interviews
• Host a live Q&A
• Post behind-the-scenes videos
• Share your company culture
• Re-use top posts
• Poll your audience
• Retweet a mention every day
• Create a branded hashtag
• Host a giveaway and invite people to comment
• Host a giveaway and invite people to tag a friend
• Host a giveaway and invite people to share your post
• Host a giveaway with other brands
• Celebrate national or international events
• Create a huge image on your Instagram profile with multiple posts
• Create a Twitter moment
• Create a Slideshare presentation (and share it)
• Curate third-party content
• Post self-explanatory images
• Post photos of your product
• Post infographics
• Post audio recordings
• Post slideshow videos
• Post tutorial or tips videos
• Post a 360 photo or video
• Livestream an event
• Live-tweet an event
• Upload videos directly to social media platforms (vs YouTube)
• Create landscape videos
• Create square (or letterbox) videos
• Create portrait videos
• Create short 10-15s videos
• Create long 20-30min videos
• Add captions to videos
• Add music to videos
• Use a cover video for Facebook
• Boost your top posts
• Use a photo of a person
• Test the carousel ad format
• Test the video ad format
• Test stories ads
• Test Messenger ads
• Test Snapchat Geofilters
• Test Snap Ads
• Sequence your Facebook ads

Collaboration

• Share user-generated content
• Share customer stories
• Sponsor a micro-influencer (sponsored posts)
• Create a piece of content with a micro-influencer and share it together
• Host a social media takeover
• Do a social swap
• Host social media events with another brand
• Host a roundtable with experts in your industry
• Hire an agency for a social media campaign
• Start a Twitter chat
• Create (and link) a Facebook Group
• Create a LinkedIn Group
• Reply to all mentions
• Use Facebook Messenger
• Create a Facebook Messenger bot
• Use a social media management tool
• Use a social media analytics tool
• Use Facebook’s preferred audience feature
• Offer time-limited discounts
• Ask your CEO (or a colleague that is well-known in your industry) to share your posts

How do you run social media experiments?

Having a framework for running social media experiments can be very helpful. Here’s one I like (though you can tweak it however much you like):

• Analyze and learn

I’m curious about how you run your social media experiments. Do you use a framework or system? Do you use any tools to help you with it? Let’s chat in the comments section below.

The amazing featured image is by chuttersnap , taken from Unsplash .

Did you find this article helpful? You might also like our all-you-need social media toolkit.

The all-you-need social media toolkit

Publish Flawlessly. Analyze Effortlessly. Engage Authentically.

Buffer is the all-you-need social media toolkit that lets you focus on doing what you love for your business.

Related Articles:

23 Top Social Media Sites to Consider for Your Brand in 2024

Find out the most popular social media sites today. Some will be familiar, others may not. Discover some that might be great for your brand but that you have yet to explore....

The Best Time to Post on Facebook in 2024: A Complete Guide

We analyzed more than 1 million Facebook posts sent through Buffer to pinpoint the best time, day, and post format for maximum engagement....

Is It Okay To Cross-Post Content On Different Platforms?

If you're wondering whether cross-posting is a good strategy for your business, the answer is yes. This guide will provide helpful cross-posting tips and real examples from other small businesses....

5 Steps to Creating Your Social Media Marketing Strategy for 2024 — From Scratch

Whether you want to grow your brand through social media or to level up as a marketer, developing a social media marketing strategy is essential. Here’s one way to do it....

140,000+ small businesses like yours use Buffer to build their brand on social media every month

• No credit card required
• Cancel anytime

May we suggest

Picked for you.

Send us an email

How to run and measure social media experiments

Written by by Jamia Kenan

Published on  February 22, 2023

Reading time  7 minutes

You already know that social moves fast. What worked for your brand a few months ago may not be relevant today. This is why social media managers thrive when they embrace a mindset of continual learning and development. Improving your social media marketing strategy requires frequent reevaluation and iteration, and running social media experiments is an essential part of the process.

Whenever you have a hypothesis, question or challenge related to your social media marketing strategy, social media experiments can provide actionable next steps. Their results provide concrete evidence to support your case for more resources or reasoning behind switching up your current content.

Social media experiments not only challenge your current strategy, but can also open opportunities to try something different—such as a new social media network or feature—and determine if it’s effective for your target audience. Experimentation can also reveal faster ways to reach your goals, help you avoid costly mistakes and uncover new information about your audience.

Grab your metaphorical safety goggles, lab coat and test tubes because in this article we’re going to walk through the steps for running and measuring successful social media experiments.

7 Steps for running a social media experiment

With these seven steps, you’ll be testing on social media with ease in no time:

• Formulate a hypothesis
• Choose the right type of social media experiment
• Select your metrics and the network you want to test
• Define the duration of the social media experiment
• Select your variables and control
• Conduct the social media experiment
• Analyze and share the results of your experiment

 1. Formulate a hypothesis

Before you begin, you’ll need a basic understanding of the following:

• The overall goals of your business
• Your current social strategy, including overarching goals per platform
• Your audiences by social network
• Your current social performance
• The questions, notions and ideas you wish to test

Prioritize a hypothesis that will result in the biggest impact on your team’s top-level social media goals . Avoid running several tests at once because it can lead to inconclusive results, especially if you’re focused on managing organic social.

If you’re using Sprout, you can learn about your audiences and performance by channel through our cross-network reports (like the Post Performance Report) or competitor reports (like the Instagram Competitors Report).

To dive even deeper into understanding your audience, use Sprout’s Advanced Listening tools. With Listening, you can build queries to track and analyze social conversations, pin down trends and view consumer sentiments. Seeing the data behind what your audience is talking about and the content they engage with will help you formulate a hypothesis.

2. Choose the right type of social media experiment

Now that you have a hypothesis, it’s time to select the type of social media experiment you will conduct to prove your theory.

There are two main types you can choose from: A/B testing and multivariable testing.

Social media experiment ideas for A/B tests

One of the most common types of social media experiments, an A/B test is an experiment where you change only one variable and keep everything else the same. These types of tests are an excellent way to pinpoint improvements that will make a measurable impact. Some common A/B tests on social include:

• Content types: video vs. a link, photo, GIF, etc.
• Captions: long vs. short
• Copy: question vs. statement, emojis or hashtags
• Images: illustrations vs. photography or animation
• Posting time: Monday at 9:00 a.m. vs. Friday at 4:00 p.m.

For example, if you wanted to test which content type is the most engaging on Instagram Stories, your team could test photo content against video content. The content type would change, but you would use the same caption and post at the same time and day of the week, one week apart.

Using Sprout, the Atlanta Hawks ‘ social team tested a casual approach to videos at community events. A player shot a hand-held video that was compared to the performance of more produced social videos. The casual video format proved to be more successful and sharing the performance data was a major win for the social team.

Social media experiment ideas for multivariable testing

As its name implies, multivariable testing alters two or three variables at once. However, since you’re experimenting with more elements, analyzing and interpreting data can be harder. You’ll also need a large audience to avoid skewing the test.

Some multivariable tests include:

• Short-form animated video vs. long-form live action video
• Varying tones of voice paired with or without emojis
• Multiple call-to-action buttons with different featured images
• Different content types with various captions
• Same content type but different days/times and platforms to see which resonates the most, like Instagram vs. TikTok

Sprout’s social team conducted several multivariable tests to help develop our TikTok marketing strategy , as you’re about to read in the next step.

3.  Select your metrics and the network you want to test

Establish the key metric you want to measure successful content against. This can include impressions, traffic to a particular page such as your brand’s website or a gated resource, and engagement metrics (Think: likes, clicks, comments or shares).

The channel you choose to conduct your experiment will depend on what you’re testing and the social media network you use the most to post that kind of content. Use your network-specific data to inform this decision. Read some of Sprout’s Insights resources to learn which content types perform the best on which platforms.

• Smart steps for content development
• 1 video, 47 uses: Maximizing your Instagram content
• Sponsored posts: How to create effective sponsored content

When our social team started testing TikTok, the main goal was to increase awareness among our target audiences. Accordingly, we selected impressions, video views, profile views and audience growth as key performance indicators.

4. Define the duration of the social media experiment

Don’t fall into the common mistake of not defining a time frame for your social media experiment. Remember that social media strategy is a long game–give time for new initiatives to grow and develop.

Your reporting window depends on your budget, audience size and KPIs, but the most important factor is to reach statistical significance.

Statistical significance refers to the likelihood your test results are the outcome of a defined cause and not chance. To reach statistical significance, you’ll need a large sample size and a control. For example, a sample size of 1,000 is stronger than 100, and your control would be the piece of content you do not change.

Set a duration and look for statistical significance. What are the significance changes? After your testing period, consider optimizing content that didn’t work during that timeframe instead of hitting the breaks on posts that aren’t resonating immediately.

While experimenting with TikTok, the social team reported results after four months since there was enough data available to analyze. They also set a weekly update to our internal social dashboard to continue testing and learning, along with iterating strategy, if needed.

During the first four months, we discovered views for every TikTok remained consistent, with an average of 535 views per video. We were also able to confirm our thoughts/assumptions about the For You Page (FYP) and the TikTok algorithm—each consistently pushed out content to our target audience (social media specialists, managers, digital marketers, etc.).

5. Select your variables and control

If you’re using A/B testing, consider all of the elements of your content that could influence your test results to ensure you’re only testing one variable. Also select your control, which is the content that will not change. For example, if you’re testing images, make sure to not change the copy, audience, timing, etc.

In our social team’s multivariable TikTok experiments, they tested several variables including formats, themes and creative considerations like music, sounds and closed captions.

In the example below, 91% of views came from the FYP, 5% came from a personal profile view and 1% came from direct followers–confirming their hypothesis that the FYP and the algorithm were the key drivers pushing out content to our target audience.

@sproutsocial It’s no secret that social teams are on the path to extreme burnout. @J A Y D E shares why it’s time for leaders to take action. #foryou #socialmediamarketing #socialmediamanager #socialmediatips #socialmedia #foryoupage ♬ Cloudy Sky – Tundra Beats

If you use Sprout, you can use tagging to track the performance of your control and the test post.

6. Conduct the social media experiment

Now it’s time to execute! Use Sprout’s Publishing tools to seamlessly plan, create, optimize and post your content for the experiment. For example, you can use Sprout’s ViralPost® technology to post at optimal send times.

Use the Tag Performance Report to organize, run and analyze your social media experiment results, including your paid campaigns.

Read our guide on creative testing for more tips and examples for conducting social media experiments.

7. Analyze and share the results of your experiment

Review the results of your experiment to identify new opportunities or add insights to your records.

If you’re trying to gain executive buy-in, especially for further testing or resources, you’ll need to communicate and create an effective data story to highlight why your company will benefit from your suggested next steps.

Using Sprout, you can easily access automated, presentation-ready reports to help illustrate your data story. Create custom reports, like this Facebook Performance Summary that includes impressions, engagements, post link clicks and publishing behavior for various content types:

Use experiments to optimize engagement and growth

Here’s a quick overview of the seven steps:

Good luck on your journey to embracing curiosity and thinking like a scientist—your social strategy will thank you.

This article is an excellent first step, but there’s so much more to learn about social media experiments. Step into the (virtual) lab yourself and get a hands-on experience, by signing up for a free trial .

• Social Media Engagement
• Social Media Strategy

Creating a 30-Day Social Media Plan

• Social Media Trends

Post Performance Report: Brands turning IRL events into online engagement

What social media tasks to automate and what to personalize

• Social Media Content

31 free social media templates that will elevate your workflows

• Now on slide

Build and grow stronger relationships on social

Sprout Social helps you understand and reach your audience, engage your community and measure performance with the only all-in-one social media management platform built for connection.

Subscribe to our Newsletter

Top insights from five social media experiments.

In this article, Amanda Wood, Social Media Marketing Manager at Hootsuite examines five social media experiments and their findings. 

According to Hootsuite, more than half a billion new users joined social media platforms over the last year, taking the global total to a whopping 4.33 billion by April 2021.

As brands continue to tap into the rapid growth of social media, it’s easy to get overwhelmed trying to keep up with emerging social networks and new competition – overlooking the need to challenge assumptions and fine-tune details to better connect with the most important person of all, the customer.

As the social media marketing team at Hootsuite, we often wonder how different approaches to posting can change the outcome – so lately, we’ve been conducting some experiments. Through them, we’ve learned a lot that might drive social marketers to think about the experiments they might want to run themselves, and how they can tweak their own strategies based on our results!

Do Links Affect Engagement and Reach in Tweets and LinkedIn Posts?

Do tweets with links get less engagement and less reach.

In our Twitter experiment , we set out to see if linkless tweets versus linked tweets would result in higher engagement. As expected, linkless tweets were the winner as many linked tweets include a call-to-action (CTA).

My colleague Nick Martin explained it perfectly – “When there’s no CTA, there are no expectations. We’re not trying to push anything, we’re just joining a conversation.”

Do LinkedIn Posts With Links Get Less Engagement And Reach?

Given that LinkedIn is a professional networking platform – with a not-so-limited character count – we wanted to test the use of external links specific to LinkedIn.

In this experiment , Iain Beable, Hootsuite’s EMEA Social Media Strategist discovered that on average, posts without links received 6x more reach than posts with links. However, while linkless posts had fewer shares on average, they received almost 4x more reactions and 18x more comments than the average post with a link.

When he unpacked the results, he found that quality engagement boosts organic reach, it’s important to spark conversations on the platform and to understand that not all metrics (e.g. shares, comments, likes) on the platform are equal. On LinkedIn, the need to provide valuable content is imperative

How Effective are “Black Hat” Instagram Tricks?

Want to buy instagram followers here’s what happens when you do.

Have you ever been followed by a bot? It seems like many small brands or influencers have started to use bots in hopes of making themselves look more legitimate or to secure sponsorships. Paige Cooper  explains this   simply:

Paige decided to try two services, and in 48 hours she had received two sets of 1,000 followers using test accounts on Instagram. However, it may come as no surprise that these decreased over the course of a week and that buying fake followers is not a great idea because fake followers don’t engage with your content, they are clearly fake (with numbers and random letters), brands regularly use audit tools and may blacklist you and Instagram may suspend your account due to the violation of its terms of service.

An Instagram Automation Experiment: Trying It So You Don’t Have To

Despite websites telling us that they are safe, reliable, and effective, there is no such thing as a risk-free, legitimate Instagram automation service. The back-end of these services is also clunky and slow with little to no help from advertised support teams.

All in all, the experiment proved that putting a budget behind bots was highly ineffective.

Does Saying “Link in Bio” Affect Your Instagram Post Performance?

We found that captions that included “link in bio” performed slightly better than those without. Stacey came to the conclusion that “you can link in bio to your heart’s content, without fear of retribution by Instagram.”

Key Learnings

Sometimes we forget that social media is supposed to be, well, social! It’s not a one-way megaphone, so stop asking your audience to do something without earning their trust, to begin with.

I urge you to think about what objectives you are trying to achieve on your social platforms, understand who your audience is and what channels they’re on, get clear on the value you’re providing to that audience with your social content, and keep up with new features and trends on different platforms.

Did you like this post? Subscribe to our Newsletter!

You might also like, more from experts talk, does cross-posting youtube links on other platforms result in lower reach.

In this month’s social media experiment, we set out to find out whether posting YouTube links on other platforms results …

Do LinkedIn Pods Work? Performance Insights And Key Learnings

Instagram reels: do longer captions lead to more engagement, does boosting instagram posts increase profile reach and engagement, tiktok’s 7-second challenge: performance insights and key learnings, how do link stickers in instagram stories affect engagement.

How Do Link Stickers In Instagram Stories Affect Engagement? Hootsuite's Senior Social Marketing Manager, Amanda Wood, and her team seek …

What Is The Best Time To Post On Instagram?

Shutterstock reveals key color trends 2022, do #foryou page hashtags actually work οn tiktok, subscribe to our newsletter, recent posts, creative campaigns.

• Film and TV

To make sure you never miss out on your favourite NEW stories , we're happy to send you some reminders

Click ' OK ' then ' Allow ' to enable notifications

Man paid private investigator to follow him for a month and people are shocked by what they found out

The experiment saw the man hire a private investigator to see what he could find out, and the results were shocking.

Kit Roberts

A YouTuber conducted an experiment in which he hired a private investigator to follow him for a month.

They may be the stuff of seedy dramas , but private investigators are very much a real profession.

It's fair to say that PIs lead a considerably less glamorous or exciting life than their televisual counterparts.

Bread and butter contracts could be anything from fraudulent insurance claims and gathering legal evidence to tracing birth parents or looking into theft at companies .

But yes, they are sometimes hired to follow a spouse who is suspected of cheating.

However, one YouTuber decided to test just how good private investigators are at their job when they're hired to shadow someone.

And what better way to do that than to hire them to shadow yourself and see what they find out?

That's exactly what YouTube prankster Max Fosh did, and the result of the investigation he launched against himself left him shocked.

Fosh got the investigator to shadow him for a full month, before checking in to see what they found out.

To make sure that the experiment wasn't tarnished by the investigator knowing from the get go why they'd been hired, he had a friend hire the investigator for him, then went about his business for a month.

He also showed that even hiring a PI can be a tricky business.

The experiment concluded and Fosh finally got to meet the person who had been shadowing him for the last few weeks.

You would expect such an encounter to be unsettling, but Fosh was still left shocked.

The information that came back showed some general footage of him going about his daily life.

This included shopping and playing some five-a-side football with his friends.

The investigator explained that he often works with people who want to catch their cheating spouse , so day to day observation was fairly simple.

Fosh was shaken when he saw the investigator for the first time, and didn't recognise him.

The investigator then rattled off Fosh's full name, parents' names, his address, and showed him some of the pictures he had taken over the past month.

More alarmingly, the private investigator then told him the password to his Wi-Fi router at home, and the password to his Instagram account.

Having access to a home Wi-Fi router would open up all sorts of information as well.

Here's how it turned out:

The investigator did have some security suggestions for Fosh as well, such as putting his passwords into a password manager.

This can encrypt your passwords and make it more difficult for people like the investigator to get hold of them.

People were shocked by the PI's discoveries.

One person wrote: "This was incredibly creepy to watch yet very entertaining."

A second posted: "It was sad to see you so shaken but it's also understandable and gave you a dose of reality of how easily this can happen."

A third joked: "Bro took 'buying followers' to a whole new level."

Topics:  YouTube , Weird , Social Media , Crime

Kit joined UNILAD in 2023 as a community journalist. They have previously worked for StokeonTrentLive, the Daily Mirror, and the Daily Star.

Choose your content:

Woman discovers dark secret about who her father really is after taking DNA test 'for fun'

The woman said she was unsure how to approach the 'surreal' situation.

Woman captures 'glitch in the matrix' in wedding dress photo that has people seriously freaked out

Bride-to-be tessa coates has people questioning their reality after taking an eerie wedding dress photo.

Chilling final moments of stunt woman caught on video as she was sitting on plane's wing before fiery crash

The wing-walker was hanging upside down on the edge of the plane moments before her death.

Man whose head was crushed by 10,000lbs of stone saw dead friend while he ‘died’ for five minutes

Mike wolo had no time to escape the huge slab of granite that collapsed onto his head.

• 90s fashion ad predicting what the future would be like leaves people shocked to 'how wrong' they were
• Woman, 23, reveals how she spends $21,000-per-month salary and people are baffled
• People seeing what prison food looks like for the first time are shocked by what they eat
• People shocked by rules and contract John Cena makes his house guests follow

+49 228 95 72 20 I [email protected]

• Über Experiment
• Unsere Gremien
• Nachhaltigkeit
• Mitglied werden & Spenden
• Ehrenamtlich engagieren
• Stellenangebote & Praktika
• Infomaterial bestellen

Länder und Möglichkeiten

• Für Schulen & Lehrkräfte

Häufige Fragen und Antworten (FAQ)

• Flexibler Freiwilligendienst
• Geförderte Freiwilligendienste
• Ranchstay, Wildlife & Sustainability
• Work & Travel

Schulbesuch im Ausland

• Homestay und Volunteering

Sprachprogramme

• Aktuelle Gesuche

Anmelden als Gastfamilie

• Stellenangebote
• Bewerbungsportal
• Experiment-Community

Schüleraustausch

Von A wie Argentinien bis U wie USA – Schüleraustausch in über 20 Ländern weltweit.

Allgemeine Programminformationen

Alles rund um Anmeldung und Ablauf und die wichtigsten Fragen und Antworten.

Wir möchten Austausch für Alle möglich machen – daher vergeben wir und unsere Partner Stipendien.

Wann bekomme ich meine Gastfamilie? Wir beantworten die meist gestellten Fragen.

Für Lehrkräfte und Schulen

Interkulturelles Lernen fängt in der Schule an – wir unterstützen Sie!

Der Flexible Freiwilligendienst

Freiwilligendienste sind eine tolle Möglichkeit, mittels gemeinnütziger Arbeit mit Land und Leuten in intensiven Kontakt zu treten, die Kultur des Gastlandes kennen zu lernen und dabei ein lokales Projekt zu unterstützen. Bei den Flexiblen Programmen hast Du eine große Länderauswahl und bist zeitlich flexibel.

Länder und Projekte

Experiment bietet Flexible Freiwilligendienste in 16 Ländern auf sechs Kontinenten an.

Wir vergeben regelmäßig Stipendien für unsere Freiwilligendienste.

Sustainable Development Goals

Unsere Freiwilligendienste tragen aktiv zur Umsetzung der 17 Nachhaltigkeitsziele bei.

Der geförderte Freiwilligendienst

Bei einem geförderten Freiwilligendienst im Ausland wird ein großer Teil der Kosten durch Ministerien oder Institutionen bestritten. Die geförderten Dienste unterliegen strengeren Kriterien, einer langfristigen Planung und einem aufwendigen Auswahlprozess.

Entdecke unsere geförderten Programme ESK &  weltwärts  und deren Unterschiede. Wir stellen die beiden Programme gegenüber.

Experiment bietet verschiedene Freiwilligendienste in 30 Ländern auf sechs Kontinenten an.

weltwärts ist ein entwicklungspolitischer Freiwilligendienst, mit dem Du Dich z.B. in Afrika, Asien, Lateinamerika und Ozeanien einbringen kannst.

Europäisches Solidaritätskorps (ESK)

Das Europäische Solidaritätskorps ist ein Förderprogramm der EU und fördert Freiwilligendienst-Projekte in Europa.

Work & Explore

Du hast die Schule erfolgreich abgeschlossen, befindest Dich gerade in der Ausbildung oder im Studium oder bist Berufsanfänger*in? Du hast Lust auf ein Abenteuer? Mit Experiment gibt es viele verschiedene Möglichkeiten nach der Schule.

Halb Au Pair - Halb Sprachkurs.

Work & Travel

Arbeiten und Reisen in Australien.

Ranchstay, Wildlife & Sustainability

Tierschutz- und Nachhaltigkeitsprojekte

Wir vergeben regelmäßig Stipendien für unsere Programme.

Ferienprogramme

Die Ferienzeit sinnvoll nutzen, gleichzeitig Spaß haben, die Welt sehen und internationale Freundschaften knüpfen? Mit unseren Ferienprogrammen ist alles möglich!

Homestay & Volunteering

Gastfamilienaufenthalt - auf Wunsch mit Volunteering.

Schüleraustausch light: Besuche die High School!

Vormittags Sprachschule - nachmittags Freizeit!

Gemeinschaft, Camp-Spirit und jede Menge Spaß!

Hier kannst Du mit Pferden in den USA arbeiten.

Gastfamilie werden

Holen Sie sich die Welt nach Hause und nehmen Sie einen internationalen Gast bei sich auf! Einige der Teilnehmenden kommen nur für 1-4 Wochen, andere bleiben bis zu 10 Monate. Daher ist es das ganze Jahr über möglich, Gastfamilie zu werden.

Allgemeine Informationen

Wer kann Gastfamilie werden? Welche Voraussetzungen und Pflichten gibt es? Die wichtigsten Fragen und Antworten.

Hier geht es zum Gastfamilienbogen mit dem Sie ganz unverbindlich Ihr Interesse bekunden können.

Steckbriefe und Videos unserer Gastschüler*innen

Hier geht es zu den aktuellen Gesuchen, Steckbriefen und Videos!

Eines der vielfältigsten Länder der Welt erwartet dich! Tropische Regenwälder, rotbraune Berge im Outback, unendlich lange Sandstrände und ein Meer voller Korallenriffe. Der Kontinent auf der anderen Seite der Welt steckt voller atemberaubender Landschaften und einer einmaligen Tierwelt. Worauf wartest Du?

Welcome to Down Under!

Schüleraustausch Australien

Entdecke ein Land, das 22 Mal größer als Deutschland ist und lass Dich nicht nur vom Leben in Down Under, sondern auch von den liebenswerten Bewohnern – den Aussies – begeistern. Die Platzierung durch unsere Partnerorganisation kann sowohl in ländlichen als auch in städtischen Regionen innerhalb folgender Bundesstaaten erfolgen: A.C.T, New South Wales, Queensland, South Australia, Tasmanien oder Victoria.

inkl. Orientation in Sydney

Early Bird: Bewirb Dich frühzeitig, nur wenige Plätze verfügbar!

Impressionen aus dem Schüleraustausch Australien

Dein nächstes abenteuer wartet, auslandsjahr australien.

G'day, mate! Lass Dich vom australischen Charme mitreißen und starte ins Abenteuer Deines Lebens! Neben dem "Aussie-Slang" ist es der spannende kulturelle Mix, der Deinen Schüleraustausch zu etwas ganz Besonderem macht. Da ist zum einen der spirituelle Geist Australiens, der durch die Aborigines noch immer ein wichtiger Teil der Kultur ist, zum anderen erwarten Dich moderne Metropolen, die Dich in ihren Bann ziehen werden.

Programmdetails

• Alter: ab 15
• Aufenthalt: 3 Monate, Schulhalbjahr , Schuljahr
• Ausreise: Januar 2025
• Preis: folgt. (Preis von 2024 als Richtwert: ab 11.290 €)

Bewirb Dich jetzt!

Explore. Exchange. Experience.

Programminformationen schüleraustausch australien, programmdauer und preise.

3 Monate (1 Term)

• Beginn: Januar 2025
• Preis: folgt. (Preis von 2024 als Richtwert: 11.290 €)

Schulhalbjahr (2 Terms)

• Preis: folgt. (Preis von 2024 als Richtwert: 13.790 €)

9 Monate (3 Terms)

• Preis: folgt. (Preis von 2024 als Richtwert: 14.790 €)

Schuljahr (4 Terms)

• Preis: folgt. (Preis von 2024 als Richtwert: 16.390 €)

Soft Landing Camp: 690 €

Klimaschutzbeitrag : 280 €

Voraussetzungen

• Gute Englischkenntnisse
• Gute allgemeine schulische Leistungen
• Anpassungsfähigkeit, Aufgeschlossenheit, gute physische und psychische Verfassung
• Bereitschaft, der neuen Umgebung (Schule und Familie) Informationen und Eindrücke von Deutschland zu vermitteln

Du hast bestimmte Einschränkungen und bist unsicher, ob ein Schüleraustauschprogramm das Richtige für Dich ist? Kontaktiere uns direkt , um gemeinsam mit unseren Partnern abzuklären, ob eine Platzierung im Programm deinen Bedürfnissen gerecht werden kann.

Bewerbungsfristen

Für unsere Austauschprogramme kannst Du Dich bis zum 31.12. bei einer Sommerausreise im Juli des folgenden Jahres bewerben und bis zum 01.08., wenn Du im Winter bzw. Januar des Folgejahres ausreisen möchtest. In einigen Fällen ist auch noch eine Bewerbung nach der Bewerbungsfrist möglich - sprich uns dazu gerne einfach an!

Natürlich gilt aber, dass eine möglichst frühe Bewerbung von Vorteil ist, um sich einen Platz zu sichern - das gilt vor allem für sehr beliebte Programmländer mit limitierten Plätzen.

Übrigens: Für unsere Stipendien gelten meist frühere Fristen. Solltest Du also Interesse an der Bewerbung um ein Stipendium haben, dann checke die Bewerbungsfristen auf unserer Stipendienübersicht für den Schüleraustausch .

In den Programmgebühren enthalten

Vor der Abreise:

• Umfassende Beratung und Betreuung durch Experiment
• Persönliches Kennenlerngespräch in Wohnortnähe
• Sicherungsschein gemäß §651r BGB
• Ausführliche Informationen zur Visumsbeantragung
• Dreitägigies überregionales Vorbereitungsseminar
• Elterninformationsveranstaltung
• Schüler*innenhandbuch
• Elternhandbuch
• Experiment T-Shirt

Während des Programms:

• Hin- und Rückflug mit einer renommierten Linienfluggesellschaft in der Economy-Class (genaue Angaben, z.B. über die Reiseroute, erhalten Sie mit den Reiseunterlagen) inkl. Zubringerflüge ab ausgewählten Flughäfen innerhalb Deutschlands oder Rail & Fly in der 2. Klasse
• Reisekosten innerhalb des Gastlandes bis zur Gastfamilie und zurück
• Orientierungsveranstaltung in Sydney (bei Ausreise im Sommer)
• Kranken-, Unfall- und Haftpflichtversicherung
• Australische Pflicht-Krankenversicherung OSHC
• Unterkunft und Verpflegung in einer Gastfamilie
• Betreuung durch die australische Partnerorganisation
• Eigenes Health & Safety-Team in Deutschland (verschiedene Präventionskonzepte unseres Vereins: Code of Good Practice, Vertrauensperson, Notfalltelefon etc.)
• Telefonischer 24-Stunden-Notfallservice durch Experiment (deutschsprachig)

Nach dem Programm:

• Viertägiges überregionales Nachbereitungsseminar
• Teilnahmezertifikat
• Möglichkeit der ehrenamtlichen Mitarbeit inkl. Seminaren und Schulungen

Nicht in den Programmgebühren enthalten

• Taschengeld, mind. 300 Euro/Monat
• Schuluniform (ca. 300 – 500 AUD)
• evtl. anfallende Schultransportkosten
• school levies,  800 – 1500 AUD pro Semester (vor Ort zu bezahlen)
• Kosten für einen eigenen Laptop bzw. Mietgebühren für einen Laptop zur Nutzung für schulische Zwecke
• evtl. anfallende Zusatzkosten für eine im Gastland notwendige Quarantäne bei Einreise
• evtl. anfallende Zusatzkosten für erforderliche COVID-19-Tests
• Klimaschutzspende

Eignung für Personen mit eingeschränkter Mobilität

• Viertägiges Vorbereitungsseminar in Deutschland
• Dreitägiges Nachbereitungsseminar in Deutschland

Als gemeinnützige Austauschorganisation ist Experiment die Vergabe von Stipendien ein ganz besonderes Anliegen. Die Ausgaben aus unserem eigenfinanzierten Stipendien-Fonds für Schüleraustauschprogramme waren in den letzten Jahren stets außergewöhnlich hoch. In diesem Programmjahr vergeben wir für den Bereich "Schüleraustausch weltweit" ein Stipendienvolumen von 100.000 Euro. Wir unterstützen damit viele Teilnehmende dabei, ihren Traum von einem Auslandsaufenthalt verwirklichen zu können (#AustauschfürAlle). Darüber hinaus vergeben wir Stipendien in Kooperation mit verschiedenen Stiftungen sowie in Zusammenarbeit mit dem Deutschen Bundestag.

Für den  Schüleraustausch in Australien  bieten wir neben unseren Teilstipendien auch folgende Stipendien an:

• Social Media Stipendium
• Zwei AJA-Stipendien in Höhe von bis zu 50% der Programmkosten

Mehr Infos zu den Stipendien und zur Bewerbung

Du möchtest selbst entscheiden, wohin es geht?

Australien choice.

Du möchtest am anderen Ende der Welt einer bestimmten Sportart nachgehen oder ganz bestimmte Fächer in der Schule haben? Dann ist unser Choice-Programm genau das richtige für Dich! Denn hier hast Du die Möglichkeit, Dich vorab für einen bestimmten Schulbezirk zu entscheiden, der all Deinen Ansprüchen an einen Schüleraustausch in Australien gerecht wird.

EXPLORE. EXCHANGE. EXPERIENCE.

"jede reise hat ein ende, aber die erinnerung daran ist unvergänglich. ".

Die Australier*innen gelten als gastfreundlich und weltoffen. Das wirst Du auch in Deiner Gastfamilie merken. Stell Dich auf eine Reihe von Ausflügen und Familienfeiern ein, denn Du wirst schnell ein fester Teil Deiner australischen Familie sein und mit ihnen gemeinsam viel erleben!

Was wirst Du alles erleben? Sieh selbst!

Max' abenteuer in australien.

Max macht einen Schüleraustausch in Australien und nimmt uns mit auf seine Reise. Vom Vorbereitungsseminar über den Hinflug bis hin zu seinen Erlebnissen vor Ort: Die Kamera ist immer mit dabei - wie hier auf Fraser Island.

Flugreisen und Klimaschutz - Wie passt das zusammen?

Unser beitrag zum klimaschutz.

Überzeugt von der Idee, dass das Erleben fremder Kulturen zum Abbau von Vorurteilen und mehr Toleranz beiträgt, bieten wir seit mehr als 90 Jahren interkulturellen Austausch an. Die damit verbundenen Reisen haben leider einen Nachteil: Sie sind oft mit langen Flügen verbunden, die die Umwelt belasten . Wir sind uns der Verantwortung bewusst, die wir als Austauschorganisation tragen und bieten daher Möglichkeiten an, den interkulturellen Austausch mit uns zusammen klimaschonend durchzuführen.

Tolle Mischung aus Lernen, Arbeit und Freizeit

Demi pair in ecuador.

Während das Leben in der Gastfamilie und die Arbeit mit den Kindern einen Einblick in den ecuadorianischen Alltag ermöglicht, gibt es an den freien Tagen in Quito und Umgebung z.B. Vulkane, Lagunen und Museen zu entdecken. Die Sprachschule ist mit der Zeit zu meinem persönlichen Lieblingsort geworden, denn dort konnte ich nicht nur meine Lernziele erreichen, sondern auch Freundschaften mit den Lehrkräften und anderen Schülern schließen. Ich würde das Programm immer wieder auswählen.

Frühstück mit Elefanten

Freiwilligendienst in südafrika.

Mein 3-monatiger Freiwilligendienst in Kapstadt auf dem SARDA Hof war bis jetzt die beste Zeit in meinem Leben. Wer Tiere liebt und gerne neue, nette Menschen kennenlernt ist dort perfekt aufgehoben. Neben meinen abwechslungsreichen Tätigkeiten bei den Ponys und im Stall konnte ich am Wochenende Kapstadt und dessen Umgebung erkunden. Ich würde die Reise mit Experiment jedes Mal wieder tun.

Viele neue Erfahrungen

Schüleraustausch in estland.

In Estland findet man trotz der Größe, alles wonach man sucht: Natur, Geschichte, Kultur, Wissenschaft und wundervolle Menschen. Ich habe mein Austauschjahr in Saku, einem Vorort der Hauptstadt Tallinn, verbracht. Zusammen mit meiner Gastschwester bin ich in Tallinn zur Schule gegangen. Zusätzlich wurden von der Partnerorganisation viele Trips geplant, sowohl ins benachbarte Finnland oder Lettland als auch innerhalb von Estland. Durch diese Trips hatte ich die Möglichkeit, Estland besser kennenzulernen und genauso auch die anderen Austauschschüler*innen. In dem Jahr habe ich Estland, die Menschen, die Kultur und auch die Sprache in mein Herz geschlossen.

Gemeinsam Erinnerungen schaffen

Durch eine Anzeige in der Zeitung wurde meine Familie auf  Experiment aufmerksam. Da wir schon eine große Familie sind, hatten wir kein Problem, sondern Freude daran, noch eine weitere Person aufzunehmen. Meine Zwillingsschwester und ich haben uns sehr gut mit unserer Austauschschülerin verstanden. Wir haben ähnliche Interessen, Hobbys und auch den Humor geteilt. Von Anfang an hatten wir ein gutes Verhältnis zueinander und bei Problemen hatten wir gegenseitig immer ein offenes Ohr. Wir konnten zusammen tolle Erinnerungen schaffen, die wir alle niemals vergessen werden!

Ferien, die man nicht so schnell vergisst

Homestay in kalifornien.

Meine drei Wochen Homestay & Volunteering im sunny California waren mega schön. Während der Zeit habe ich nicht nur neue Menschen, eine neue Kultur und den Alltag der US-Amerikaner*innen kennengelernt, sondern ich konnte mich auch sprachlich super weiterentwickeln. Rückblickend kann ich nur sagen, dass es eine unvergessliche Zeit war und ich es nur allen empfehlen kann.

Von Irland nach Costa Rica

Schüleraustausch in 2 ländern.

Mein Auslandsaufenthalt war trotz der Corona-bedingten Ungewissheit und dadurch notwendigen Spontaneität eine unglaublich wundervolle Erfahrung. Ich wollte eigentlich ein Jahr nach China, was aber auf Grund der Corona-Pandemie abgesagt werden musste. Ich konnte noch auf ein Halbjahr in Irland wechseln, mit dem Ziel, das zweite Halbjahr in China zu verbringen. Als sich die Situation nicht besserte, ergab sich kurzfristig die Möglichkeit, nach Costa Rica zu gehen. Ich wurde dabei von Experiment stets flexibel und hilfsbereit unterstützt. In beiden Ländern habe ich mich super wohl gefühlt und konnte so gleich zwei neue Kulturen kennenlernen.

Wissenswertes rund um Deinen Schüleraustausch in Australien

Fragen & antworten.

Wie funktioniert das australische Schulsystem, welche Unterrichtsfächer gibt es zur Auswahl und welche Reisemöglichkeiten gibt es vor Ort? Wir beantworten Dir die wichtigsten Fragen rund um den Schüleraustausch Australien.

In welchen Regionen ist eine Platzierung möglich?

In unserem Australien Classic Programm lässt Du Dich überraschen, wohin genau Dich Dein Schüleraustausch verschlägt. So kannst Du z.B. in einem eher ländlichen oder in einem städtischen Gebiet untergebracht werden. Aber keine Sorge: Du kannst Dir sicher sein, dass Du nicht irgendwo im hintersten Outback landest.

Fest steht, dass Du durch unsere Partnerorganisation in einem der folgenden Bundesstaaten platziert wirst: A.C.T, New South Wales, Queensland, South Australia, Tasmanien oder Victoria.

Zur besseren Orientierung findest Du hier eine Karte.

Wie funktioniert das australische Schulsystem?

Schulen in Australien sind sehr gut ausgestattet und haben einen hervorragenden akademischen Standard. Bei den PISA-Studien belegen australische Schüler daher auch stets vordere Plätze. Besonders das gute Lehrer-Schüler-Verhältnis wird von Schüler*innen nach ihrem Auslandsjahr in Australien stets gelobt. Außerdem verfügen alle Schulen über International Departments, deren persönliche Ansprechpersonen Dir stets zur Seite stehen.

Die australische Highschool ist ähnlich aufgebaut wie eine Gesamtschule in Deutschland. Es gibt keine Trennung in Haupt-, Realschulen und Gymnasien.

Australische High School können State Schools, State High Schools oder Colleges heißen.

In Down Under gibt es zwei Schulhalbjahre, die in je zwei Terms unterteilt sind. Jeder der vier Terms eines ganzen Schuljahres umfasst etwa zehn Wochen. Danach gibt es immer Ferien! Ein Schüleraustausch in Australien bedeutet für Dich, dass Du im Regelfall mit Term-Start im Januar oder Juli beginnst. 

Normalerweise geht der Unterricht von 9 bis 15:30 Uhr. Meist gibt es an den Schulen einen sogenannten »Tuck-Shop«, bei dem Du Snacks kaufen kannst oder Deine Gastfamilie gibt Dir ein Lunchpaket mit.

Wie ein klassischer Schultag in Australien aussehen kann, zeigt Max in diesem Video !

Muss ich eine Schuluniform tragen?

Die Schuluniform gehört an den meisten staatlichen Schulen dazu. Dabei hat jede Schule ihre eigene Uniform, denn sie dient der Identifikation mit der eigenen Schule. Du musst aber nicht unbedingt eine neue Uniform kaufen, denn es gibt sie auch gebraucht an der Schule zu erwerben.

Keine Sorge, falls Dir das Tragen einer Schuluniform zu Beginn ein wenig seltsam vorkommt: Du wirst Dich schnell daran gewöhnen und bald auch die Vorteile davon zu schätzen wissen.

Welche Schulfächer erwarten mich?

In Australien gibt es eine Reihe von Pflichtfächern, die Du belegen musst. Dazu zählen unter anderem Englisch, Mathematik und Sport. Darüber hinaus kannst Du aber eine Menge spannender Wahlfächer belegen.

Außergewöhnliche Unterrichtsfächer, die es in Deutschland nicht unbedingt gibt, sind zum Beispiel: Meeresbiologie, Bush Walking, Outdoor Education, Touristik, Fotografie, Grafik, Soziologie, sogar Surfen kannst Du in der Schule belegen – entdecke ganz neue Interessen!

Gibt es die Möglichkeit, in Australien zu reisen?

Längere Schulferien sind im Dezember und Januar. Aber nicht nur in dieser Zeit gibt es eine Vielzahl von Reiseangeboten der Partnerorganisation bzw. der Gastschulen. Von Outback-Safaris über Reisen nach Sydney oder Cairns bis hin zu Trips nach Fraser Island oder ins Northern Territor y – für jede*n ist etwas dabei!

Diese Erlebnisse machen Deinen Schüleraustausch Australien zu einem unvergesslichen Abenteuer!

Wir sind für Dich da:

Gerne helfen wir Dir auch persönlich weiter. Individuelle Beratung, offene Fragen oder besondere Wünsche?   Dann melde Dich bei uns!

Nancy Hörig

Weitere spannende schüleraustauschprogramme.

Schüleraustausch USA Classic

High School USA: Das beliebteste Land für einen Schüleraustausch! Egal, ob Michigan, Texas, Kalifornien oder Pennsylvania, die US-amerikanischen Gastfamilien heißen Dich in jedem der 50 Staaten der USA herzlich willkommen. Den „American Way of Life“ erfährst Du auf seine ganz eigene Weise überall hautnah.

Schüleraustausch USA Choice

Nimm das Ruder selbst in die Hand und wähle, wo Du Deinen Austausch in den USA verbringst. Denn beim USA-Schüleraustausch Choice entscheidest Du, wohin es geht. Es gibt kaum ein anderes Land wie die USA, welches sich so sehr durch seine kulturelle und landschaftliche Vielfalt auszeichnet. Wähle jetzt!

Schüleraustausch Tschechien Classic

Du wirst in Tschechien schnell das Gefühl haben, wie zu Hause zu sein, da unsere tschechischen Nachbar*innen sehr gastfreundlich sind und großen Wert auf ihre Traditionen und Familienbande legen. Von Anfang an werden sie dafür sorgen, dass es Dir…

Los geht's!

Entdecke die welt mit experiment.

Du willst mit Work & Travel den Sommer Deines Lebens in Australien verbringen? Oder bei einem flexiblen Freiwilligendienst in Ghana mit Kindern arbeiten? Oder bei einem Schüleraustausch in den USA Cowboy-Feeling hautnah erleben? Das und vieles mehr bieten Dir unsere Programme – da ist für jeden das Richtige dabei!

Beratungstermin vereinbaren

Astrid Schrader

+49 228 95722-10

[email protected]

Mein Experiment Newsletter

Stay up to date.

Bleib informiert und verpasse keine Neuigkeiten, Angebote, Inspirationen und Gewinnspiele von Experiment. Wir verschicken 1x im Monat unseren Newsletter, den Du jederzeit wieder abbestellen kannst.

Bitte fülle Deine Bank-verbindung in Deinem Profil aus.

Sie sehen gerade einen Platzhalterinhalt von Facebook . Um auf den eigentlichen Inhalt zuzugreifen, klicken Sie auf die Schaltfläche unten. Bitte beachten Sie, dass dabei Daten an Drittanbieter weitergegeben werden.

Sie sehen gerade einen Platzhalterinhalt von Instagram . Um auf den eigentlichen Inhalt zuzugreifen, klicken Sie auf die Schaltfläche unten. Bitte beachten Sie, dass dabei Daten an Drittanbieter weitergegeben werden.

Sie sehen gerade einen Platzhalterinhalt von X . Um auf den eigentlichen Inhalt zuzugreifen, klicken Sie auf die Schaltfläche unten. Bitte beachten Sie, dass dabei Daten an Drittanbieter weitergegeben werden.

Find the images you need to make standout work. If it’s in your head, it’s on our site.

• Images home
• Curated collections
• AI image generator
• Offset images
• Backgrounds/Textures
• Business/Finance
• Sports/Recreation
• Animals/Wildlife
• Beauty/Fashion
• Celebrities
• Food and Drink
• Illustrations/Clip-Art
• Miscellaneous
• Parks/Outdoor
• Buildings/Landmarks
• Healthcare/Medical
• Signs/Symbols
• Transportation
• All categories
• Editorial video
• Shutterstock Select
• Shutterstock Elements
• Health Care
• PremiumBeat
• Templates Home
• Instagram all
• Highlight covers
• Facebook all
• Carousel ads
• Cover photos
• Event covers
• Youtube all
• Channel Art
• Etsy big banner
• Etsy mini banner
• Etsy shop icon
• Pinterest all
• Pinterest pins
• Twitter all
• Twitter Banner
• Infographics
• Zoom backgrounds
• Announcements
• Certificates
• Gift Certificates
• Real Estate Flyer
• Travel Brochures
• Anniversary
• Baby Shower
• Mother’s Day
• Thanksgiving
• All Invitations
• Party invitations
• Wedding invitations
• Book Covers
• Editorial home
• Entertainment
• About Creative Flow
• Create editor
• Content calendar
• Photo editor
• Background remover
• Collage maker
• Resize image
• Color palettes
• Color palette generator
• Image converter
• Contributors
• PremiumBeat blog
• Invitations
• Design Inspiration
• Design Resources
• Design Elements & Principles
• Contributor Support
• Marketing Assets
• Cards and Invitations
• Social Media Designs
• Print Projects
• Organizational Tools
• Case Studies
• Platform Solutions
• Generative AI
• Computer Vision
• Free Downloads
• Create Fund

How to Run a Successful Social Media Experiment

The one constant of social media is change. Take a look at the standard Facebook or Twitter feed five years ago and compare it to yours today: the type of content that connects with users is always changing. Once you develop your social media marketing strategy, the easiest way to ensure your brand becomes irrelevant is to stick to the plan. To grow, you must constantly alter your content delivery and stay in touch with the people who value your brand.

Have you ever contemplated how a brand like Buzzfeed or Upworthy seemingly took over the internet in a couple of years? The answer is simple: they embrace zealous use of social media testing. Upworthy, in particular, writes 25 headlines for each article and tests them all before settling on one. You may not ultimately adjust your workflow to write 25 headlines for each piece of content you share, but the blueprint for success is there. When you conduct a social media experiment you will learn more about the type of content that increases engagement. Even with a small staff you can run social media tests regularly to hone in on the key elements of a winning social media post for your brand.

Are you ready to jump in? Before you craft your first A/B test , get some context:

Don’t Confound Your Variables

Before we detail actual social media tests that significantly grow your audience, you’ll need a quick statistics overview. Running a successful social media experiment necessitates the dilution of your data: if you run an experiment and don’t accurately interpret the results, the test is a waste of time and resources.

First, you’ll have to determine the one variable that you’ll be testing in your social media test. Many first-time experimenters who are unfamiliar with introductory statistics may accidentally test multiple variables at a time without intending to.

For example, let’s say you want to change the image in your Facebook post but leave everything else identical. Great! That’s a wonderful way to test the power of the images you’re using. However, if you post these two variations at different times of the day, you’re unintentionally testing the timing of the post as well, which will reduce the reliability of your results. Double check that there is only one differentiating factor in your social media experiment. This variable can be the time of day, but if so the posts should be identical in every other way.

Determine How to Measure Your Success

So you’ve settled on what variable to test. How do you measure how successful your two different social posts are? If you’re conducting a test on Twitter by modifying your profile picture or information, you’ll be looking for an increase in followers. But when conducting an A/B test with two different post, you should choose what metric you believe most accurately reflects success for you.

In some cases, this will be the amount of shares the post garners. Maybe it’s the most retweets, or the most overall conversions from the post itself. This will vary slightly from test to test – use your best judgment and choose the metric for your own means.

Outside factors like Facebook’s algorithm can determine how many impressions, or views, your ad makes in a given amount of time. Don’t rely on simple impressions as a determining factor for success – action is a more reliable metric that you can measure and increase.

Calculate If Your Result Is Significant

So, you’ve run your experiment and you’ve determined that, with post A, 88 people shared it out of 500 that saw it. For social post B, 190 people shared it for the 1000 that viewed it. It seems that, even when you account for the extra views on post B, percentage-wise it increased engagement. So, should you adopt the changes you made to post B going forward in your social media strategy?

The answer lies in crunching the numbers to learn if your results are statistically significant. The actual formula for calculating significance is fairly complex, utilizing standard deviations and other statistics measurements to determine a result. Luckily, for us marketers, there are multiple online tools that can quickly tell us what our results mean.

Two popular options for determining significance are tests from KissMetrics and VWO . We’ve inputted the hypothetical results for the above test into the Get Data Driven test via KissMetrics to yield the following results:

In this case, the results are not statistically significant. The extra shares on post B are not enough to warrant a change in strategy. You would need to run additional tests to determine if that variable can increase your engagement or conversion.

Now that you have a handle on how a social media test is run, let’s cover a few tests that marketers love.

1. Alter Headlines to Increase Clicks

No matter how engaging and well-written your promoted content is, it will get lost in the shuffle if the headline doesn’t generate excitement. Trying out different headlines to get more clicks and views is perhaps the bedrock of social media testing, and is why, for better or for worse, writing the best headline has become an exact science.

Once you determine if you’d like to increase clicks or shares, write two compelling titles and determine what’s “different” about post B, the one you’re testing. Twitter is an ideal platform for testing headlines – with no images needed, you can focus on what text connects to users scrolling through their feed.

Split testing your headlines and tweeting both options at roughly the same time will provide you with enough data to determine which headline is best. Choosing the best headline, as David Ogilvy famously noted, should be how you spend 80 cents of every dollar. Make your headlines value-driven and include more actionable verbs to draw more readers in. By testing your headlines regularly you will learn what phrasing connects to social media users currently.

2. Tweak Design to Appeal to the Senses

There is plenty of science and research dedicated to determining the best colors to utilize in your social media marketing. This lengthy post from Conversion XL delves into multiple case studies for call-to-action buttons, examining what colors work best.

Often, on Facebook, Instagram, and other platforms, you won’t have a standard button in your advertising, but you’ll still need to determine what color connects most with your audience. By adjusting the design (mainly, the color or typography) of your post, you can measure users’ engagement with that color in the context of your brand.

When you set up your design-based social media experiment, take one of two routes: either, test colors that are diametrically opposed (like green versus red), or colors that are very close (seafoam versus teal). Going for two tests is generally best: test colors that aren’t similar whatsoever, then once you have a template color try tweaking it slightly to further optimize your success metric.

3. Shift Image Focus to the Customer or the Product

This classic social media experiment is largely informed by your past marketing experience for your brand. While keeping the copy of your ad on Facebook (or whatever platform) identical, choose two separate images to split test. Answer this fundamental question: for your brand, do users respond positively when they see the advantages of the product, or a satisfied customer?

Cloud-based technologies or conceptual services sometimes see more success when their product is humanized. Featuring a smiling associate or giddy customer in your ad may prove more effective than a generic image of a cloud with an arrow. However, for brands that offer visually iconic products (food, cars, clothing) seeing the product is often more important for the customer. And of course, context is everything. As Hootsuite determined in their own social media tests, images of body parts other than faces saw more engagement overall for their brand.

Running this test is a starting point for many successful social media ads. Note that, with the amount of posts and related content your brands generate, these tests should serve as a guide, not an all-encompassing mandate. Don’t eliminate product-based imagery altogether based on one social media marketing test.

4. Reword Your Call-to-Action to Raise Conversion

For social media marketing, your call-to-action is arguably the most important aspect of your post. When you directly appeal to viewers to engage with your brand, the phrasing is paramount. What tactic actually drives clicks, shares, and purchases? As with all industries, the answer is brand-specific. By testing multiple calls-to-action you can find the verbs and the tactics that connect well across the board.

Many brands today are thinking outside of the box, using absurd humor or an extremely casual tone to sound authentic. Breaking down the barrier between your brand and the customers is a great method of gaining clicks. But it’s only one of many methods. When testing modified calls-to-action, test two of the following strategies against each other:

• Use humor to break down the barrier. Here’s a great example  from Canva.
• Demonstrate how your customer’s life will improve with your product. Citing studies or satisfied customers always helps. Such as: “90% of our customers stay asleep throughout the night. Are you ready for the best sleep of your life?”
• Appeal to the fear of missing out on something beneficial. Without your product, something negative could occur. An example: “Don’t wait until it’s too late. Contact us about cloud storage services today.”

5. Consider the Time of Day to Expand Your Audience

With the above social media marketing experiments, the two posts should generally be posted at the same time of day and be visible to different users. However, to nail down the most beneficial social media marketing strategy, you should always determine what times of day and what days in the week see the greatest traction for your posts.

If you use a product like Audiense Community Manager , you can determine the exact times of the week that your audience is online, and schedule posts accordingly to reach the maximum pairs of eyes per post and grow your audience as fast as possible. If you’d prefer to run tests the old-fashioned way, you can use the following guidelines from KissMetrics :

• If you’d like the maximum amount of retweets, try tweeting at 5 pm on Wednesdays or on weekends. If you want to test morning engagement, use these times as your control and proceed accordingly.
• To boost your overall click-through-rate (CTR), try posting either at noon or at 6 pm.

Perfecting your social media posting calendar should be your top priority before testing small differences in content.

With this road map you can finetune your marketing efforts online and reach more users by crafting engaging content. As you test new images and budget accordingly, The Shutterstock Editor will prove incredibly helpful: choose from our vast collection of stock images and create a compelling visual social media post in minutes.

Recently viewed

Related Posts

10 Genius Print Advertising Ideas (with Examples and Tips)

If you thought print was dead, think again! Print advertising…

Website Design Ideas, Examples, and Inspiration

A good website design merges usability with aesthetic style. We’ll…

21 Tasteful Logo Color Combinations for Modern Brands

Pick the perfect color combination for your logo color palette…

Branding Package Ultimate Guide (with Examples)

Bringing together all of your brand identity elements into a…

© 2023 Shutterstock Inc. All rights reserved.

• Terms of use
• License agreement
• Privacy policy
• Social media guidelines

• Open access
• Published: 13 March 2024

Effects of a 14-day social media abstinence on mental health and well-being: results from an experimental study

• Lea C. de Hesselle 1 &
• Christian Montag 1  

BMC Psychology volume  12 , Article number:  141 ( 2024 ) Cite this article

4514 Accesses

5 Altmetric

Metrics details

Background and aim

The study investigated the effects of a 14-day social media abstinence on various mental health factors using an experimental design with follow-up assessment. Hypotheses included positive associations between problematic smartphone use (PSU) and depression, anxiety, fear of missing out (FoMO), and screentime. Decreases in screentime, PSU, depression and anxiety, and increases in body image were assumed for the abstinence group. Additionally, daily changes in FoMO and loneliness were explored.

Participants completed different questionnaires assessing PSU, FoMO, depression and anxiety, loneliness and body image and were randomized into control and social media abstinence groups. Daily questionnaires over 14 days assessed FoMO, loneliness, screentime, and depression and anxiety. 14 days after the abstinence, a follow-up questionnaire was administered. Multilevel models were used to assess changes over time.

PSU was positively associated with symptoms of depression, anxiety and FoMO, but not with screentime. Spline models identified decreased screentime and body image dissatisfaction for the intervention group. Depression and anxiety symptoms, PSU, trait and state FoMO, and loneliness, showed a decrease during the overall intervention time but no difference between the investigated groups could be observed (hence this was an overall trend). For appearance evaluation and body area satisfaction, an increase in both groups was seen. Daily changes in both loneliness and FoMO were best modelled using cubic trends, but no group differences were significant.

Results provide insights into effects of not using social media for 14 days and show that screentime and body image dissatisfaction decrease. The study also suggests areas for future studies to better understand how and why interventions show better results for some individuals.

Peer Review reports

Social media is part of everyday life with 4.76 billion users worldwide and a 3.0% annual increase [ 1 ]. The average time spent on social media is 2.5 hours, totalling 5 hours of screentime per day [ 1 ]. Simultaneously, there is a global rise in mental health issues with a 25% increase in anxiety disorders and a 28% increase in depressive symptoms, primarily affecting young adults [ 2 ]. It has already been discussed if the increase in social media use paved the way for the increase is psychopathologies, but establishing causality remains difficult [ 3 ]. Despite this, studies have linked problematic social media use to problems such as symptoms of depression and anxiety, [ 4 , 5 , 6 ] stress, negative body image and low physical activity [ 7 , 8 , 9 , 10 ].

While most studies use cross-sectional data, assessing changes over time in longitudinal data is necessary. The present study combines a longitudinal design and experimental approach to evaluate effects of a 14-day social media abstinence on several mental health factors.

Problematic smartphone use (PSU)

Smartphones enable various activities (e.g., communication, entertainment, gaming, online surfing or using social media). Excessive smartphone use which can lead to adverse consequences has been termed problematic smartphone use (PSU, [ 11 , 12 ]) This includes relying on the smartphone to regulate one’s mood, experiencing agitation in its absence, and unsuccessful attempts to reduce usage [ 11 , 12 ].

PSU has been associated with different negative life and health outcomes such as poor sleep quality, [ 13 , 14 , 15 ] impaired work and academic performance, [ 16 , 17 , 18 , 19 ] neck and shoulder pain, [ 20 , 21 ] and visual impairment [ 22 , 23 ]. Further, PSU has been positively associated with depression, anxiety, and Fear of Missing Out (FoMO) [ 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Though simple cross-sectional associations do not allow causal interpretation, according to the Compensatory Internet Use Theory (CIUT, [ 31 ]) excessive smartphone use can be interpreted as a coping mechanism for dealing with life stressors and negative emotions. Seen this way: associations between negative affect and overuse of technology might exist due to “self-medication” principles, although such medical language needs to be further investigated regarding its fit in the realm of Internet Use Disorders [ 32 ]. Another theoretical framework which is often used in PSU research is the Interaction of Person-Affect-Cognition-Execution model (I-PACE, [ 33 , 34 ]). This model describes different core characteristics and dispositional factors (personality, history of psychopathology, genetics, etc.) which can impact on how situations are received and what the response is (cognitive biases, certain affective responses), thereby contributing to the development of PSU. It offers insights into, for example, how stressful situations might lead to heightened smartphone use (in detail: use of certain applications) as a coping mechanism for dealing with stress. Of note, the I-PACE model also presents a history of psychopathologies such as depression as a vulnerability factor within the P-variable to develop excessive online use patterns. Hence, much of the variables investigated and introduced later in this manuscript could be seen through the lens of the I-PACE model: in particular, we mention that the intervention aiming at reduction of social media use could trigger changes in cognitive and affective processes which in turn might result in lower psychopathological tendencies as recorded via several variables in the present work (e.g. depressive tendencies or body image dissatisfaction).

Though most aforementioned studies assess PSU via self-report questionnaires, some others have used objective measurements of smartphone use (screentime, screen unlocks) and found either no association between depression and screentime [ 27 ] or an inverse relationship between depression and number of screen unlocks, [ 27 , 35 ] indicating even a lower unlock frequency for depressed compared to non-depressed individuals. However, objective and subjective measures of smartphone use are only moderately associated [ 27 , 36 ].

Social media use

Social media use presents one specific form of spending (excessive) time on the smartphone as platforms enable users to share real-time pictures, videos, and other content, facilitating connections through likes, comments, and multimedia messages. A lot of daily smartphone screentime is spent on social media, potentially leading to adverse consequences. Problematic social media use (PSMU) shows itself in symptoms similar to PSU, however it applies especially to social media use. This includes maladaptive behaviours such as escalating time spent on social media and unsuccessful efforts to reduce usage, resulting in negative consequences for the user. One can see from the symptoms that an addiction framework will be used for the present work, although PSMU could also mean very different behavior such as cyberbullying online.

PSMU in terms of an addictive behavior (not officially recognized) has been linked to different health outcomes. Koc and Gulyagci [ 37 ] and Hong et al. [ 38 ] found that depressive symptoms positively predict Facebook addiction. Koc and Gulyagci [ 37 ] further identified anxiety and insomnia as positive predictors. Additionally, FoMO was identified as a strong predictor of (problematic) social media use [ 39 , 40 ] and is also linked to both higher PSMU and lower meaning in life [ 41 ]. Furthermore, associations between PSMU and depression, anxiety, stress, higher cognitive failures [ 42 ] and poor sleep quality were found [ 4 , 5 , 6 , 43 , 44 , 45 , 46 ]. Though most studies are cross-sectional, limiting causal interpretation, some longitudinal studies have been performed. One study found a bidirectional relationship between PSMU and depression and identified PSMU as predictor of insomnia, suicide related outcomes and ADHD symptoms [ 45 ]. PSMU could also lead to negative consequences such as low academic achievement, decrease in real life social participation, [ 47 , 48 ] negative work-family balance, and decreased job performance [ 49 ].

Marino et al. [ 50 ] and Montag et al. [ 51 , 52 ] showed moderate to high associations between PSU and PSMU, resulting from an overlap of both phenomena. See also other works [ 53 , 54 ], showing robust overlap between PSU and distinct forms of social media overuse. While a lot of time is spent on social media, [ 1 ] not all smartphone use can be attributed to social media use, because smartphones also serve for gaming, browsing or video watching, instead, total screentime represents all uses. In the present study, total smartphone screentime was assessed as the original intention was to focus on smartphone gaming as well (see Procedure and Sample) and the smartphone serves as the platform for both social media engagement and gaming activities. Consequently, screentime serves as a comprehensive measure, reflecting both gaming and social media usage (but also a myriad of other activities including e-mail-checking, listening to music, etc.).

Abstinence studies

Apart from assessing smartphone use and different outcomes, as mentioned above, assessing changes in outcomes due to not using the smartphone pose a possibility to infer about the causal direction of effects to answer questions such as that abstaining from smartphone and/or social media use results in less reported clinical symptoms, e.g. in the realm of depression or eating disorders. Several studies explored the effects of social media abstinence. Radtke et al. [ 55 ] found significant decreases in screen time during and after the intervention, mixed results on life satisfaction, decrease in anxiety and stress, improvement of sleep quality and mixed effects on FoMO and loneliness. However, the authors argue that different implementations of abstinence and measurements might account for the heterogeneity of findings. Another review by Fernandez et al. [ 56 ] found similar effects: increase in life satisfaction, affective well-being, decrease in perceived stress, and an increase in boredom, craving and time distortion.

Further studies – some with experimental designs – found a decrease in FoMO, increase in mental well-being and social connectedness, [ 57 ] and decreased depression and anxiety [ 58 ]. However, Vally and D’Souza [ 59 ] found a decrease in well-being, an increase in negative affect and loneliness during intervention and a nonsignificant increase in stress for the experimental group. Brailovskaia et al. [ 60 ] assessed if a full abstinence is necessary to see improvements in mental health or if a reduction of one hour per day would be enough. They found increased well-being and positive lifestyle changes in both experimental groups with stronger effects in the reduction group.

The effect sizes found in the mentioned studies are small to moderate with just few large effects.

Most of the aforementioned studies employed 7 days of abstinence with some exceptions where an abstinence of 14 days was implemented. Also, the foci of these studies were mainly on mental health variables like depression, anxiety, and FoMO. While these are key variables in the present study, another goal is to assess effects of social media abstinence on body image.

Research questions and hypotheses

This study aims to assess the effect of a 14-day social media abstinence on different mental health and well-being factors using an experimental design. A follow-up assessment 14 days after the end of the intervention was implemented to assess stability of effects. A single 14-day follow-up was chosen due to economic reasons as retaining study participants is harder, the longer a study runs. Also, previous studies [ 55 , 58 , 60 , 61 ] have realised different periods between end of intervention and follow-up (e.g. 48 hours, 4 days, 1 week, 1 month and 3 months), so using 14 days is somewhere in between, economically feasible and of the same length as the intervention period. Daily questionnaires were used to analyse changes during the intervention period.

The following hypotheses and research questions will be evaluated.

The hypotheses H1, H2.1 and H2.2 are based on baseline data collected before randomization into different groups.

H1: In the overall sample, PSU is positively associated with reported total screentime, depression and anxiety symptom severity, and FoMO, respectively.

Previous studies (but not all) showed a moderate positive association between PSU and objectively measured screentime [ 27 , 36 ]. Although participants manually input screentime (total smartphone use, not just social media), similar low to moderate associations can be expected. The present study should also be able to replicate positive associations between PSU and depression and anxiety symptoms, and FoMO.

H2.1: In the overall sample, screentime is positively but weakly associated with depression and anxiety scores, FoMO, and loneliness, respectively.

H2.2: In the overall sample, more screentime is negatively associated with body image.

Many of these associations have not been shown with screentime, but with (problematic) smartphone use [ 37 , 38 , 39 , 40 , 43 ]. This study did not assess (problematic) social media use. Instead, the variable of interest is screentime in association with different mental health outcomes. However, since a large amount of screentime also in the present participants is spent on social media, [ 1 ] it should be associated with the mentioned variables as well. Nonetheless, the correlations should be small, as Huang [ 62 ] showed in a meta-analysis that the time spent on social network sites is only weakly correlated with psychological wellbeing.

H3: Screentime decreases in the experimental group.

Since a good portion of screentime is spent on social media [ 1 ] an abstinence should reflect in overall decreased screentime. Total screentime was chosen as the measurement, because it reflects both time spent on social media and other smartphone activities.

H4: Depression and anxiety scores, and PSU scores decrease in the social media abstinence group.

Depression, anxiety, and PSU have been positively linked to problematic social media use [ 37 , 38 , 40 , 43 , 50 ]. So, reducing – or eliminating – social media should lead to decreasing symptoms. Additionally, previous abstinence studies showed decreased anxiety, stress and depression scores [ 55 , 60 ].

H5: Body image improves in the abstinence group.

Body image is negatively associated with social media use, as exposure to idealized body types and social comparison in particular on visual driven social media platforms could lead to body dissatisfaction [ 7 , 63 , 64 ]. Although social media is not the only factor contributing to a negative body image, [ 65 , 66 ] abstaining from it is likely to improve body image by reducing the exposure to social comparison.

RQ1: How does FoMO change over time?

Previous studies reported mixed results concerning changes in FoMO, [ 55 , 57 ] possibly due to different intervention durations. Potentially, FoMO increases during the first few days of social media abstinence and then decreases once participants adapt to not using social media to check up on their friends. This study aims to provide insights into the changes over time during the abstinence phase by assessing FoMO daily and comparing different trends over time.

RQ2: What is the impact of abstinence on loneliness?

Several studies assessed the effect of social media abstinence on loneliness and found mixed results [ 55 , 56 , 57 , 59 ]. Since loneliness was assessed daily, changes over the duration of abstinence can be detected and different trends can be compared.

RQ3: Are the changes observed during abstinence stable after the intervention?

Positive and negative changes due to abstinence from social media were already mentioned, but not much is known about the stability of these changes over time. Brailovskaia et al. [ 61 ] reported stable effects of changes after a 14-day gaming abstinence, however not much is known about stability after social media abstinence. Stability will be evaluated through change in scores between the end of intervention and the follow-up.

This study took place between October 2022 and February 2023. Participants were recruited via different university mailing lists, flyers posted around the university buildings, social media and eBay marketplace and underwent assessments outlined in Fig.  1 . Inclusion criteria were: legal age (18+), good knowledge of the German language, and use of smartphone and social media. This online study was conducted using the SurveyCoder website, [ 67 ] with questionnaires administered at baseline, daily, end of intervention and at follow-up. Participants received a daily link to the website via email at 4 pm. After the baseline questionnaires, participants were randomized into four groups and received intervention instructions. Since no tracking apps were used, the deinstallation of apps was not monitored. Participants were allowed to use their smartphones as normal for all other purposes and were only instructed to deinstall apps from their smartphones (other devices were not mentioned in the instruction). At the end of the intervention, participants were allowed to reinstall apps and were asked how they intend to manage their future social media consumption.

Schematic procedure. The procedure of the study is presented in the figure, the abbreviations for the included questionnaires are presented in the questionnaire section of this work

Originally, comparisons between all groups were planned, however due to data cleaning steps (see Sample), only groups 1) and 2) were used for analyses in the main body of this manuscript.

The initial sample compromised N  = 196 participants who provided combined datasets (baseline, daily, end of intervention and follow-up). After exclusion of non-users of social media or gaming apps in the experimental groups, a sample of n  = 165 participants was left. Since this sample consisted of 83.6% females and one focus of the study was the change in body image (which mainly shows effects for women), [ 68 ] only the female participants were analysed further.

From our view, this led to a too small group size for the gaming abstinence group to run robust statistics ( n  = 21). Since negative consequences due to gaming are mostly prevalent in men [ 69 ] and a small group size compared to the other groups can be problematic in analyses, this group was excluded from the main body of this manuscript (but see Supplement ). The combined abstinence group ( n  = 31) was also excluded as this would have been relevant to provide insights in particular in comparison to both the distinct gaming and social media abstinence groups. But since the gaming abstinence group was excluded, it was decided to exclude this from the manuscript as well (again, for more information see Supplement ).

Thus, the effective sample consisted of n  = 86 female participants which were randomized into control group ( n  = 35) and social media abstinence group ( n  = 51). Most participants held A-level qualifications (64.0%) or university degrees (29.1%) and were currently enrolled at university (77.9%). Groups were comparable in terms of age (m control = 23.17, s control = 6.99; m socmed =24, s socmed =4.63; t(54.233) = -0.61, p = .54), education (majority have A-levels (63% in control group; 65% in social media abstinence group); followed by university degree (28% in control group; 29% in social media abstinence group), \({\chi }^{2}\) (3) = 1.47, p = .69), and current occupational status (77% university students in control group; 78% university students in social media abstinence group; \({\chi }^{2}\) (4) = 3.33, p = .50).

Analyses including excluded groups are presented in the Supplementary Materials 1 and 5  -  10 .

Questionnaires

Fear of missing out.

Trait and online specific state FoMO was assessed using the TS FoMO scale [ 70 ] at baseline, end of the intervention and follow-up. Participants were asked to rate their agreement to 12 statements on a 5-point Likert scale (1 = “strong disagree”, 5 = “strong agree”). Mean scores for both subscales were computed and showed high internal consistency at all timepoints ( \(\alpha\)  = 0.76 – 0.83 and \(\alpha\)  = 0.77 – 0.79, respectively). The German version as provided by Wegmann et al. [ 70 ] was used in the present work.

Daily FoMO was assessed using a single item question (FoMOsf; [ 71 ]): “Do you experience FoMO (the fear of missing out)?” Riordan et al. [ 71 ] proposed this single item assessment which showed good validity. Participants rated how much this applied to them on the current day on a 5-point Likert scale (1 = “no, not true of me”, 5 = “yes, extremely true of me”).

Problematic smartphone use

PSU was assessed using the Smartphone Addiction Scale – Short Version (SAS-SV; [ 72 ]) where participants rated their agreement with different statements concerning their smartphone use. These statements include difficulty concentrating, agitation in the absence of the smartphone, persistent preoccupation with the device, exceeding intended use duration, frequent checking behaviour, experiencing physical discomfort during use, and missed work obligations due to excessive smartphone use. Agreement was provided on a 6-point Likert scale ranging from 1 = “strongly disagree” to 6 = “strongly agree” and sum scores were used in analyses (higher values = more PSU). The scale showed high internal consistencies at all time points ( \(\alpha\)  = 0.81 – 0.86). German version was used as in Haug et al. [ 73 ].

Depression and anxiety symptoms

Depression and anxiety symptoms were assessed using the 4-item Patient Health Questionnaire (PHQ-4; [ 74 ]). Participants were asked how often in the past seven days (for daily measurements: on the current day) they experienced different symptoms of depression or anxiety. Answers ranged from 0 = “no, not at all” to 3 = “nearly every day” (“nearly the whole day”) and were summed with higher values indicating more severe symptoms. The PHQ-4 demonstrated high internal consistency at all time points ( \(\alpha\)  = 0.81 - 0.87).

How often participants experienced loneliness and isolation from others was assessed using the German version of the UCLA 3-item loneliness scale [ 75 ] as in Montag et al. [ 76 ]. Answers were given on a 3-point Likert scale (1 = “hardly ever”, 3 = “often”) and summed with higher values indicating higher loneliness. The scale demonstrated good internal consistency ( \(\alpha\)  = 0.78 – 0.89).

Body Image Dissatisfaction (BID) was assessed using the BIAS-BD, [ 77 ] which presents two rows of schematic body figures ranging from 60% to 140% of the average BMI, separated for sex. Participants chose the figure best representing their actual and ideal body. Percentages were transformed into BMI equivalents and a BID score was computed as the difference between actual and ideal body size.

Further, the MBSRQ-AS [ 78 ] was used to measure different body image dimensions on 34 items: appearance evaluation (How content people are with their appearance), appearance orientation (How much attention people pay to their own appearance), body area satisfaction (How satisfied they are with different areas of their body), overweight preoccupation (How concerned they are with their weight and staying thin), and self-classified weight (How they would rate their own weight and how other people would rate their weight). Scores were summed for each dimension, and all showed good internal consistency with \(\alpha =\) 0.66 – 0.92.

Participants were asked to open the screentime feature on their smartphones and type the hours and minutes into the questionnaire. Values were converted into minutes for analysis. At baseline and follow-up, the screentime from the last seven days was averaged to represent the average daily screentime at baseline and follow-up, respectively. No differences were made between smartphone operating systems.

Fear of COVID-19

Fear of COVID-19 (FCV) was assessed at baseline using the FCV19S [ 79 , 80 ] to use as a covariate in analyses. This was due to the study being conducted amid the COVID-19 pandemic (October 2022 to February 2023), allowing for the proper consideration of various pandemic-related constraints in the analyses. The FCV19S demonstrated a good internal consistency of \(\alpha\)  = 0.83. Participants were asked to rate seven statements concerning their fear of COVID-19 on a 5-point Likert scale from 1 = “strongly disagree” to 5 = “strongly agree”. Scores were summed for analysis and higher values indicated more Fear of COVID-19. The German version used herein was by Fatfouta and Rogoza [ 80 ].

Further questionnaires

The following questionnaires were assessed but not included in the main analyses. They are included in the supplement (see Supplementary Material ): IPAQ (physical activity; [ 81 ]), PANAS – positive affect subscale, [ 82 , 83 ] Perceived Stress Scale (PSS-4; [ 84 , 85 ]) and satisfaction with life scale (SWLS; [ 86 , 87 ]). The cited German versions were used for all scales.

Data analysis

Data analysis was performed using R version 4.1.3 [ 88 ]. Apart from descriptive statistics at baseline, correlations were computed using Holm’s correction for p -values.

To model trends in outcome variables (PSU, FoMO, screentime, depression/anxiety, loneliness, body image), multilevel models were used. For the variables measured at three time points (baseline, end of intervention, follow-up), spline models were used with the knot point set to the end of intervention. This allows assessment of change between baseline values and end of intervention and between end and follow-up. Further, RQ3 can be answered using these models. For an explanation on spline models in the multilevel modelling framework, see Grimm et al. [ 89 ]. First, only the trend over time was modelled for the total sample (called model 1 for each outcome). Then, covariates were added (baseline FCV, PSU, and BID for MBSRQ-AS outcomes), and group differences were accounted for using dummy coded variables (0 = control, 1 = abstinence group; called model 2 for each outcome). Random intercepts were used for all models to allow for interindividual differences in values and where possible, random slopes were used to allow for interindividual differences in change over time. The R-packages lme4 version 1.1.28 [ 90 ] and lmerTest version 3.1.3 [ 91 ] were used.

For daily measured outcomes, several multilevel models were computed. First, a linear trend over time for all experimental groups was evaluated. This model included baseline PSU, FCV, and the respective baseline value of each outcome. Then, the group variable was added to a separate model. To further assess changes over time in FoMO and loneliness (RQ1 and RQ2), different trends over time were assumed (quadratic, cubic) and models were compared using AIC, BIC and Likelihood Ratio Tests.

Datasets and analysis scripts are available at the Open Science Framework https://osf.io/qdp8r/ .

The present study was approved by the university’s ethics committee (Ethics committee of University of Ulm) under application number 252/22.

Descriptive statistics at baseline are presented in Table 1 . Due to randomization, no group differences were expected, and t-tests were non-significant.

Correlations

Correlations at baseline are presented in Table 2 with Holm corrected p -values and 95% confidence intervals.

Multilevel models

Results from models for outcome variables measured at three time points are presented in Table 3 .

For depression and anxiety symptoms, model 1 with fixed slope showed a significant negative trend before the knot point (b = -0.60, t(160.76) = -2.52, p  = .013), but no change afterwards. Model 2 showed no significant change over time or differences between groups.

Model 1 for screentime showed nonsignificant changes across all time points. Upon incorporating covariates, no significant changes over time were observed for the control group. However, there was a significant difference in change between baseline and end of intervention between control group and abstinence group, as evidenced by one-sided testing (b = -38.905, t(159.8) = -1.685, p  = .094/2 = .047). This indicates a decrease in screentime in the abstinence group. See Fig.  2 for a graphical representation of mean values in both groups.

Plot of main results: changes in Body Image Dissatisfaction (BID) and average daily screentime

Using PSU as outcome, model 1 showed a significant negative linear trend before the knot point (b = -4.023, t(150.54) = -5.583, p  < .001) and a nonsignificant negative trend after the knot point. There was random variance for the trend over time. In model 2, only baseline FCV was added as covariate and had a significant influence on PSU. Further, the pre-knot negative trend was significant for the control group and no differences between groups were seen.

Model 1 for loneliness showed a significant negative linear change between baseline and end of intervention (b = -0.66, t(139.39) = -4.751, p  < .001), indicating an overall decrease in loneliness during the intervention. In model 2, the negative linear change between baseline and end of intervention was significant for the control group and there were no group differences.

For state FoMO, model 1 showed a significant negative trend between baseline and end of intervention (b = -0.22, t(161.11) = -3.813, p  < .001) and no change between end of intervention and follow-up. Model 2 showed no significant trend over time in the control group and no differences between groups.

There was a significant negative trend in model 1 for trait FoMO for the change between baseline and end of intervention (b = -0.52, t(157.63) = -7.30, p  < .001) and no change afterwards. Model 2 showed a significant negative pre-knot point trend for the control group. This trend did not differ between groups, however the difference in values at the knot point (end of intervention) was significantly lower for the abstinence group.

The first model assessing trend over time showed a nonsignificant decrease in BID between baseline and end of intervention and no change afterwards. Model 2 showed no significant change over time for the control group. However, the difference in change between baseline and end of intervention was significant for one-sided testing (b = -0.95, t(139.64) = -1.900, p  = .0595/2 = .029), indicating that BID values decreased more for the social media abstinence group compared to the control group. See Fig.  2 for a graphical representation of the mean values of both groups.

In model 1, appearance evaluation showed a significant increase between baseline and end of intervention (b = 0.988, t(161.02) = 2.736, p  < .001) and no change afterwards. In model 2, the positive change between baseline and end of intervention was only significant for the control group.

Overweight preoccupation showed no change over time in model 1. Upon adding covariates and a group variable, there was a significant negative trend for the change between baseline and end of intervention in the control group. No group differences were found.

Model 1 revealed an almost significant increase in body area satisfaction during the intervention (b = 0.698, t(136.24) = 1.899, p  = .0597) and no change afterwards. However, this trend was not significant in model 2 with covariates and group variable, nor was there a difference between groups.

No effect of either time or group could be identified for self-classified weight and appearance orientation.

Daily data models

For the daily data models, different trends were modelled for each variable. As covariates in all models the respective values at baseline were used as well as baseline FCV19, PSU, and BID. Results are provided in Table 4 .

For the total sample, linear (model 1) or quadratic (model 2) trend over time for screentime could not be found. Upon adding the group variable in the quadratic trend model (model 4), the interaction term for linear trend and abstinence group was almost significant (b = 8.56, t(1046.18) = 1.800, p  = .072), as well as the interaction between the quadratic trend and the abstinence group (b = -0.61, t(1045.28) = -1.727, p  = .084). This indicates a different change in screentime for the social media abstinence group than observed in the control group.

Linear (model 1) and quadratic (model 2) trends for changes in loneliness were not supported for the total sample. Model 3 assumed a cubic trend and found significant results for the linear, quadratic and cubic parts of the trend. Model comparisons between three models identified model 3 as the best fitting model (AIC = 3596.9, BIC = 3647.2, M2 vs. M3:  \({\chi }^{2}\) (1) = 6.78, p  < .01).

Models 4 (linear trend and group) and 5 (quadratic trend and group) showed no trends over time nor group differences. Model 6 – including a cubic trend – showed significant linear, quadratic and cubic trends for the control group and no differences between groups. This model fit the data best (AIC = 3597.2, BIC = 3667.6, M5 vs. M6:  \({\chi }^{2}\) (2) = 9.92, p  < .01). However, there was no significant difference between models with and without the group variable (M3 vs. M6: \({\chi }^{2}\) (4) = 7.6911, p  = .1036).

Model 1 showed no significant linear trend over time in depression and anxiety symptoms. Model 2 included the linear trend over time and the group variable and showed an almost significant negative change for the control group (b = -0.04, t(1048.51) = -1.834, p  = .067) and a significant interaction between daily change and the abstinence group (b = 0.06, t(1046.40) = 2.429, p  < .05), indicating different changes over time between both groups.

Model 1 found a significant negative linear trend with an average 0.0195 decrease per day for FoMO. Model 2 included a quadratic trend as well as the linear trend and found the linear trend to be significant (b = -0.054, t(1043) = -2.506, p  < .05). Model 3 assumed a cubic trend and found this to be significant. Model comparisons identified model 3 as best fitting (AIC = 2862.6, BIC = 2918.0, M2 vs. M3: \({\chi }^{2}\) (1) = 19.116, p  < .001).

Model 4 found a significant negative linear trend for the control group and no group differences. Model 5 (quadratic trend) found no significant linear or quadratic trend for either group. Model 6 (cubic trend) found a significant linear, quadratic and cubic trend for the control group, but no difference between the groups. Model 6 was identified as the best fitting model containing the group variable (AIC = 2866.4, BIC = 2941.9, M5 vs. M6: \({\chi }^{2}\) (2) = 20.22, p  < .001), however the fit was not significantly different from model 3 (M3 vs. M6: \({\chi }^{2}\) (4) = 4.1824, p  = .3819).

The aim of this study was to evaluate the impact of a 14-day social media abstinence on different mental health and well-being variables and body image. Results are discussed below.

Associations between variables

The study’s findings align with the expectations outlined in H1. PSU demonstrated a weak positive association with screentime, although it was not statistically significant, which is consistent with prior research [ 27 , 36 ]. This supports the notion that self-reported PSU and screentime is not necessarily the same construct and that screentime is not an appropriate measure for PSU. Furthermore, different uses and motives for smartphone use can explain why some people have high screentime but low PSU.

Motives can be evaluated using the CIUT [ 31 ]. Though literature on associations between smartphone use motives and screentime is not exhaustive, studies have found that motives like mood regulation and enjoyment are positively associated with PSU, whereas information seeking and socializing are less likely to have an influence on addictive behaviour in the realm of smartphones [ 92 , 93 ]. Additional motives for use were distress tolerance and mindfulness, [ 94 ] FoMO, [ 95 , 96 ] and boredom proneness [ 96 , 97 ].

PSU was assumed to be positively associated with depression and anxiety symptom severity (H1) and a moderate association (albeit not significant for this sample) has been found. Again, this is consistent with previous literature [ 24 , 25 , 26 , 27 ].

The hypothesized association between PSU and state FoMO was highly positive whereas the association with trait FoMO was moderately positive, supporting H1. Both can be interpreted as people experiencing more PSU symptoms also experience more FoMO. Again, these results are in accordance with previous studies identifying FoMO as a correlate of PSU [ 25 ]. Furthermore, according to the I-PACE model [ 33 , 34 ] trait FoMO can be seen as a core characteristic impacting how certain situations are received and responded to, thus, contributing to the development of PSU (please note that due to the overlap with neuroticism, it might be also seen as a trait; [ 98 ]).

Positive but weak associations were found for screentime and depression/anxiety symptom severity, FoMO, and loneliness, supporting H2.1. All associations are low (to moderate for screentime and depression/anxiety) and not significant in the present sample. This was expected, as Huang [ 62 ] reported very small associations between time spent on social network sites and mental health variables. There are different uses of smartphones that can be unproblematic but lead to high screentimes (e.g attending online meetings or using the phone to study). This should be controlled for in future studies.

Hypothesis H2.2 assumed a negative correlation between screentime and body image. This hypothesis is supported only descriptively, as no correlation is significant. Screentime showed weak negative associations with appearance evaluation and body area satisfaction, and positive associations with appearance orientation (see that also using objective screentime-measures, a recent work by Rozgonjuk et al. [ 64 ] established links between longer smartphone use and higher body dissatisfaction; in this work also patients with eating disorders were investigated). The present findings suggest that individuals who spend more time on their smartphones are a little more appearance oriented and a little less satisfied with their bodies. However, it is crucial to note that screentime and exposure to online media are not the sole factors influencing BID [ 65 , 66 ]. Studies found that the type of screentime influences development of BID, at least for TV or computers [ 99 , 100 , 101 ]. Specifically, computer use for leisure activities was positively associated with BID whereas computer use for homework showed negative associations with BID [ 99 ]. Hrafnkelsdottir et al. [ 100 ] found positive correlations between gaming, TV/DVD/internet watching and BID and low correlations between BID and online communication. This suggests that different uses of smartphones and social media might have different impacts on body image. An assessment of motives of use could provide further insight.

Changes over time

An overall decrease in screentime during the intervention, especially for the abstinence group was found, supporting hypothesis H3. Since a large portion of screentime is spent on social media, [ 1 ] abstinence from selected applications should be reflected in overall decreased screentime. These results align with previous abstinence studies which also reported decreased screentime [ 55 , 57 ]. However, on a day-to-day basis during the intervention, no significant changes in screentime were found. This could be attributed to fluctuating screentimes or compensatory behaviour, such as switching to other apps to fill the time.

Depression and anxiety scores decreased when assessing the total sample but there was no change nor difference between groups when considering the group variable. Therefore, H4 is not supported for depression and anxiety. Contrary to the hypothesis, daily models showed a decrease in the control group and an increase in the experimental group (please note that these observations are on a descriptive level only and changes were not pronounced). However, according to the CIUT, [ 31 ] smartphones and by extension social media can act as a coping mechanism and as an escape to handle negative emotions and daily hassles. If this outlet is unavailable, symptoms of depression and anxiety might increase (we are not of the opinion though that social media use should be seen as an effective way to deal with one's own problems and it is unclear how long lasting the effect around depression and anxiety would be). Motives of use are often evaluated in gaming research and escapism was identified as a strong predictor for gaming time (and gaming disorder, [ 102 ]), highlighting the tendency of dealing with negative emotions by escaping into an online world [ 103 ].

Additional analyses were conducted to examine the relationship between changes in depression and anxiety scores and baseline PSU, across groups (total sample). The results indicated a weak negative correlation, suggesting that, across groups, individuals with higher baseline PSU scores experienced more decrease in depression and anxiety scores compared to those with lower PSU scores. Since there was only a minimal difference in baseline PSU scores between the experimental groups (see Table 1 ), the association between baseline PSU values and change in depression and anxiety scores cannot be the reason for the different trends over time measured in the depression and anxiety scores.

However, when baseline depression and anxiety scores were correlated with changes in anxiety and depression scores, a moderate negative correlation emerged. The control group displayed slightly higher baseline scores than the abstinence group, although this difference was not statistically significant. This provides a possible explanation to the reduction in depression and anxiety scores in the control group compared to the abstinence group.

For PSU, an overall decrease was found during and after the intervention. However, there were no differences between groups. Possibly, the study attracted individuals seeking to change their social media habits as it was advertised as an abstinence study. Intention towards future social media use was assessed at the end of intervention and follow-up with most participants expressing a desire to reduce their social media time (end of intervention: 57% in control group, 49% in abstinence group; follow-up: 48% in control group, 66% in abstinence group). Since there was no big group difference in the number of participants with this answer, controls possibly intended to reduce their social media consumption even before their study participation and changed their behaviour, thus experiencing less PSU.

Additionally, PSU is not synonymous with social media use. Though studies found a strong positive association between PSU and PSMU, [ 50 ] PSU can develop through other smartphone uses than social media. Plus, participants were asked to abstain only from selected social media but were able to freely use their phones for other uses.

Body image was assessed using different variables. Appearance orientation and self-classified weight showed no changes over time or between groups. There was an overall increase in appearance evaluation and body area satisfaction due to the intervention, but no differences between groups. Overweight preoccupation decreased for the control group and there was no difference in changes between groups.

The BID values decreased significantly more in the abstinence group than in the control group, suggesting that taking a break from exposure on social media is helpful in decreasing BID. Overall, hypothesis H5, suggesting social media abstinence improves body image satisfaction and decreases dissatisfaction, was partially supported. However, the effect is small, as social media is not the only factor influencing body image [ 65 , 66 ]. Social comparison occurs not only on social media but in real-life interactions and through other media like TV or magazines.

Daily change in FoMO (RQ1) was best modelled using a cubic trend. Nevertheless, there were no group differences, suggesting day-to-day fluctuation in FoMO regardless of whether social media apps were used or not. Previous studies found mixed outcomes regarding intervention on FoMO, [ 55 , 57 ] but only assessed it for 7 days. Since FoMO fluctuates, assessing changes over a longer period of time offers a more comprehensive dataset for fitting appropriate models.

Trait and state FoMO decreased during intervention in both groups and remained stable afterwards. The absence of group differences can be attributed to individuals using their phones for different uses that do not necessarily influence FoMO. Elhai et al. [ 104 ] found that FoMO is more associated with non-social smartphone use like entertainment, news and relaxation compared to social smartphone use. Though PSU was positively related to both trait and state FoMO, screentime was not associated with either. This suggests that simply abstaining from social media may not lead to reduced FoMO, at least not in the here investigated time interval. Furthermore, since traits are considered relatively stable constructs, [ 105 ] it is debatable if a change in trait FoMO can be expected. Trait FoMO can be also conceptualized as dispositional factor in the I-PACE model [ 33 , 34 ] and is a stable influence on the development of PSU. Since dispositional factors are not expected to change strongly – especially not in a short time frame – the observed change was more likely an artifact in data.

A cubic trend was also the best way to model daily changes in loneliness (RQ2), though there were no significant group differences. Both groups experienced a decrease in loneliness during the intervention and no change afterwards. This suggests that overall, loneliness decreased but due to factors other than not using social media apps. This study did not assess other life events, making it challenging to explain this change fully. Furthermore, previous studies found mixed effects of abstinence on loneliness [ 55 , 56 , 57 , 59 ]. Moreover, there are different motives for social media use and not all are related to social interaction. These results can also be interpreted in the context of the uses and gratification theory [ 106 ] as the smartphone can be used to fulfil individuals needs such as representation, maintaining social networks, receiving online support, relaxing, or escaping from pressures [ 107 ]. Not all motives are related to loneliness.

The majority of spline models did not show significant changes after the intervention, indicating stability in the effects and addressing RQ3. Specifically, this applies to changes in BID and screentime, where differences between the experimental and control groups were seen. For the other variables, there were no group differences, but the changes between baseline and end of intervention measurements suggested an overall decrease (depression and anxiety, PSU, overweight preoccupation, state and trait FoMO, loneliness) or increase (appearance evaluation, body area satisfaction) and no change afterwards. However, these changes apply to both groups, meaning the control group changed as well and abstinence was not the sole reason for change, but maybe the intention to reduce consumption was.

Contribution and limitations

The present study provides novel insights into the relationships between social media use and mental health and well-being. Notably, this study used an experimental design to implement a 14-day intervention and conducted a follow-up assessment 14 days after this intervention. Furthermore, this work focussed on body image and its changes over time. This can provide information for future studies or intervention designs as it shows that body image dissatisfaction can be decreased by not using social media for 14 days. The experimental approach adds depth to the understanding of the impact of social media on body image, a topic that has primarily been explored through correlative studies. The present results can also provide a first basis for inventing and implementing interventions in the realm of eating disorders or body schema disorders, as it shows that abstaining from social media might improve body satisfaction (replication of the present findings is of importance). But: As there is currently no consensus or official diagnosis for PSU and also against the limitations mentioned below, authors refrain from proposing clinical implications based on reduced screentime during intervention at the moment.

Additionally, previous studies modelled FoMO as linear change over time and often only assessed one week of change. The present study provides more detailed insight into daily FoMO as well as daily loneliness changes and found that both can be best represented using a cubic trend.

There are several limitations. First, the original study design intended to include four groups for comparison, but due to a high percentage of women in the sample, only female participants were analysed in the main manuscript, leading to exclusion of the gaming disorder groups. Consequently, small sample sizes were used for analyses, resulting in low statistical power. While some results were directionally clear, they did not reach statistical significance in the present work. Second, PSMU was not assessed alongside PSU, which should be considered in future studies. Screentime was not objectively measured but participants manually input the information from their screentime feature (hence we have an indirect objective screentime measure, which might be prone to transfer error though but was checked for plausability). In further studies, an objective measurement could be implemented by either using tracking apps – and thus validating if participants use social media apps – or asking for screenshots of the screentime feature. Measurement of total screentime and PSU were chosen as the original intention was to include the gaming and combined abstinence groups. In that case, both screentime and PSU would be acceptable measurements for all groups, as gaming and social media use can be reflected in total screentime and can both lead to symptoms of PSU.

Aside from assessing PSMU and using an objective measure for future studies it is suggested to assess motives and uses for individual’s smartphone use because this could provide further insight into why outcomes change for some participants but not for all. This could also aid in developing more nuanced interventions that properly fit a person’s needs. Lastly, different groups with different levels of abstinence could be realized. This has previously been done by Brailovskaia et al. [ 60 ] for general smartphone use.

Using a longitudinal and experimental approach to a 14-day social media abstinence, the present study was able to show significant decreases in BID and screentime due to abstinence. Further, mental well-being factors were evaluated and showed improvement over time but did not differ between groups. Using daily assessments of FoMO and loneliness, cubic trends were identified as the best way to model fluctuation in these variables. These findings provide valuable insights into the complex dynamics of social media use and its impact on mental health and well-being and can provide information to plan future interventions addressing social media/smartphone use or body image related disorders.

Availability of data and materials

Abbreviations.

Akaike Information criterion

Bayesian information criterion

Body image dissatisfaction

Body mass index

Compensatory Internet Use Theory

Fear of Missing Out

Positive affect negative affect scale

Patient health questionnaire 4

Problematic social media use

Perceived stress scale

Research question

Smartphone Addiction Scale – short version

Satisfaction with Life scale

Trait State FoMO scale

We Are Social Ldt. We Are Social UK. 2023. Cited 2023 Mar 15. The Changing World of Digital In 2023. Available from: https://wearesocial.com/uk/blog/2023/01/the-changing-world-of-digital-in-2023/ .

Forbes Health. 2023. Cited 2023 Mar 17. Mental Health Statistics. Available from: https://www.forbes.com/health/mind/mental-health-statistics/ .

Twenge JM, Joiner TE, Rogers ML, Martin GN. Increases in Depressive Symptoms, Suicide-Related Outcomes, and Suicide Rates Among U.S. Adolescents After 2010 and Links to Increased New Media Screen Time. Clin Psychol Sci. 2018;6(1):3–17.

Article   Google Scholar  

Bashir H, Bhat S. Effects of social media on mental health: a review. Int J Indian Psychol. 2016;1(4):125–31.

Google Scholar  

Dhir A, Yossatorn Y, Kaur P, Chen S. Online social media fatigue and psychological wellbeing—A study of compulsive use, fear of missing out, fatigue, anxiety and depression. Int J Inf Manag. 2018;40:141–52.

Karim F, Oyewande AA, Abdalla LF, Ehsanullah RC, Khan S. Social Media Use and Its Connection to Mental Health: A Systematic Review. Cureus. 2020;12(6). Cited 2022 Jun 28. Available from: https://www.cureus.com/articles/31508-social-media-use-and-its-connection-to-mental-health-a-systematic-review .

Fardouly J, Vartanian LR. Social media and body image concerns: current research and future directions. Curr Opin Psychol. 2016;9:1–5.

Grimaldi-Puyana M, Fernández-Batanero JM, Fennell C, Sañudo B. Associations of objectively-assessed smartphone use with physical activity, sedentary behavior, mood, and sleep quality in young adults: a cross-sectional study. Int J Environ Res Public Health. 2020;17(10):3499.

Article   PubMed   PubMed Central   Google Scholar  

Lin ML, Wang WY, Liao CC, Luo YJ, Kao CC. Examining thevel among University Students in Taiwan. Healthcare. 2020;8(4):556.

Shimoga SV, Erlyana E, Rebello V. Associations of social media use with physical activity and sleep adequacy among adolescents: cross-sectional survey. J Med Internet Res. 2019;21(6):e14290.

Billieux J, Maurage P, Lopez-Fernandez O, Kuss DJ, Griffiths MD. Can disordered mobile phone use be considered a behavioral addiction? An update on current evidence and a comprehensive model for future research. Curr Addict Rep. 2015;2(2):156–62.

Panova T, Carbonell X. Is smartphone addiction really an addiction? J Behav Addict. 2018;7(2):252–9.

Rod NH, Dissing AS, Clark A, Gerds TA, Lund R. Overnight smartphone use: A new public health challenge? A novel study design based on high-resolution smartphone data. PLOS One. 2018;13(10):e0204811.

Sohn SY, Rees P, Wildridge B, Kalk NJ, Carter B. Prevalence of problematic smartphone usage and associated mental health outcomes amongst children and young people: a systematic review, meta-analysis and GRADE of the evidence. BMC Psychiatry. 2019;19(1):356.

Yang J, Fu X, Liao X, Li Y. Association of problematic smartphone use with poor sleep quality, depression, and anxiety: a systematic review and meta-analysis. Psychiatry Res. 2020;284:112686.

Article   PubMed   Google Scholar  

Derks D, Bakker AB. Smartphone use, work-home interference, and burnout: a diary study on the role of recovery. Appl Psychol. 2014;63(3):411–40.

Hawi NS, Samaha M. To excel or not to excel: Strong evidence on the adverse effect of smartphone addiction on academic performance. Comput Educ. 2016;1(98):81–9.

Lee W Jun, Shin S. A Comparative Study Of Smartphone Addiction Drivers’ Effect On Work Performance In The U.S. And Korea. J Appl Bus Res. 2016;32(2):507–16.

Samaha M. Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Comput Hum Behav. 2016;321–5.

Kim SY, Koo SJ. Effect of duration of smartphone use on muscle fatigue and pain caused by forward head posture in adults. J Phys Ther Sci. 2016;28(6):1669–72.

Park J, Kim J, Kim J, Kim K, Kim N, Choi I, et al. The effects of heavy smartphone use on the cervical angle, pain threshold of neck muscles and depression. Adv Sci Technol Lett. 2015;91:12–7.

McCrann S, Loughman J, Butler JS, Paudel N, Flitcroft DI. Smartphone use as a possible risk factor for myopia. Clin Exp Optom. 2021;104(1). Cited 2021 May 24. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1111/cxo.13092 .

Wang J, Li M, Zhu D, Cao Y. Smartphone overuse and visual impairment in children and young adults: systematic review and meta-analysis. J Med Internet Re. 2020;22(12):e21923.

Demirci K, Akgönül M, Akpinar A. Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. J Behav Addict. 2015;4(2):85–92.

Elhai JD, Levine JC, Dvorak RD, Hall BJ. Fear of missing out, need for touch, anxiety and depression are related to problematic smartphone use. Comput Hum Behav. 2016;63:509–16.

Elhai JD, Dvorak RD, Levine JC, Hall BJ. Problematic smartphone use: a conceptual overview and systematic review of relations with anxiety and depression psychopathology. J Affect Disord. 2017;1(207):251–9.

Rozgonjuk D, Levine JC, Hall BJ, Elhai JD. The association between problematic smartphone use, depression and anxiety symptom severity, and objectively measured smartphone use over one week. Comput Hum Behav. 2018;1(87):10–7.

Thomée S, Härenstam A, Hagberg M. Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults - a prospective cohort study. BMC Public Health. 2011;11(1):66.

Wolniewicz CA, Tiamiyu MF, Weeks JW, Elhai JD. Problematic smartphone use and relations with negative affect, fear of missing out, and fear of negative and positive evaluation. Psychiatry Res. 2018;262:618–23.

Yuan G, Elhai JD, Hall BJ. The influence of depressive symptoms and fear of missing out on severity of problematic smartphone use and Internet gaming disorder among Chinese young adults: A three-wave mediation model. Addict Behav. 2021;112: 106648.

Kardefelt-Winther D. A conceptual and methodological critique of internet addiction research: towards a model of compensatory internet use. Comput Hum Behav. 2014;1(31):351–4.

Montag C, Marciano L, Schulz PJ, Becker B. Unlocking the brain secrets of social media through neuroscience. Trends Cogn Sci. 2023;27(12):1102–4.

Brand M, Wegmann E, Stark R, Müller A, Wölfling K, Robbins TW, et al. The Interaction of Person-Affect-Cognition-Execution (I-PACE) model for addictive behaviors: Update, generalization to addictive behaviors beyond internet-use disorders, and specification of the process character of addictive behaviors. Neurosci Biobehav Rev. 2019;1(104):1–10.

Brand M, Young KS, Laier C, Wölfling K, Potenza MN. Integrating psychological and neurobiological considerations regarding the development and maintenance of specific Internet-use disorders: An Interaction of Person-Affect-Cognition-Execution (I-PACE) model. Neurosci Biobehav Rev. 2016;1(71):252–66.

Elhai JD, Tiamiyu MF, Weeks JW, Levine JC, Picard KJ, Hall BJ. Depression and emotion regulation predict objective smartphone use measured over one week. Personal Individ Differ. 2018;133:21–8.

Boase J, Ling R. Measuring mobile phone use: self-report versus log data. J Comput-Mediat Commun. 2013;18(4):508–19.

Koc M, Gulyagci S. Facebook addiction among turkish college students: the role of psychological health, demographic, and usage characteristics. Cyberpsychology Behav Soc Netw. 2013;16(4):279–84.

Hong FY, Huang DH, Lin HY, Chiu SL. Analysis of the psychological traits, Facebook usage, and Facebook addiction model of Taiwanese university students. Telemat Inform. 2014;31(4):597–606.

Blackwell D, Leaman C, Tramposch R, Osborne C, Liss M. Extraversion, neuroticism, attachment style and fear of missing out as predictors of social media use and addiction. Pers Individ Differ. 2017;1(116):69–72.

Pontes HM, Taylor M, Stavropoulos V. Beyond, “facebook addiction”: The role of cognitive-related factors and psychiatric distress in social networking site addiction. Cyberpsychol Behav Soc Netw. 2018;21(4):240–7.

Montag C, Müller M, Pontes HM, Elhai JD. On fear of missing out, social networks use disorder tendencies and meaning in life. BMC Psychol. accepted;

Montag C, Markett S. Social media use and everyday cognitive failure: investigating the fear of missing out and social networks use disorder relationship. BMC Psychiatry. 2023;23(1):872.

Andreassen CS. Online social network site addiction: a comprehensive review. Curr Addict Rep. 2015;2(2):175–84.

Hussain Z, Griffiths MD. The associations between problematic social networking site use and sleep quality, attention-deficit hyperactivity disorder, depression, anxiety and stress. Int J Ment Health Addict. 2021;19(3):686–700.

Hussain Z, Starcevic V. Problematic social networking site use: a brief review of recent research methods and the way forward. Curr Opin Psychol. 2020;36:89–95.

Mok WT, Sing R, Jiang X, See SL. Investigation of social media on depression. In: The 9th International Symposium on Chinese Spoken Language Processing. 2014. 488–91.

Kuss DJ, Griffiths MD. Online social networking and addiction—a review of the psychological literature. Int J Environ Res Public Health. 2011;8(9):3528–52.

Ponnusamy S, Iranmanesh M, Foroughi B, Hyun SS. Drivers and outcomes of Instagram addiction: psychological well-being as moderator. Comput Hum Behav. 2020;1(107):106294.

Zivnuska S, Carlson JR, Carlson DS, Harris RB, Harris KJ. Social media addiction and social media reactions: the implications for job performance. J Soc Psychol. 2019;159(6):746–60.

Marino C, Canale N, Melodia F, Spada MM, Vieno A. The overlap between problematic smartphone use and problematic social media use: a systematic review. Curr Addict Rep. 2021;8(4):469–80.

Montag C, Sindermann C, Rozgonjuk D, Yang S, Elhai JD, Yang H. Investigating links between fear of COVID-19, neuroticism, social networks use disorder, and smartphone use disorder tendencies. Front Psychol. 2021;2(12):682837.

Montag C, Wegmann E, Sariyska R, Demetrovics Z, Brand M. How to overcome taxonomical problems in the study of Internet use disorders and what to do with “smartphone addiction”? J Behav Addict. 2021;9(4):908–14.

Sha P, Sariyska R, Riedl R, Lachmann B, Montag C. Linking internet communication and smartphone use disorder by taking a closer look at the facebook and whatsapp applications. Addict Behav Rep. 2019;1(9):100148.

Rozgonjuk D, Sindermann C, Elhai JD, Christensen AP, Montag C. Associations between symptoms of problematic smartphone, Facebook, WhatsApp, and Instagram use: an item-level exploratory graph analysis perspective. J Behav Addict. 2020;9(3):686–97.

Radtke T, Apel T, Schenkel K, Keller J, von Lindern E. Digital detox: An effective solution in the smartphone era? A systematic literature review. Mob Media Commun. 2022;10(2):190–215.

Fernandez DP, Kuss DJ, Griffiths MD. Short-term abstinence effects across potential behavioral addictions: a systematic review. Clin Psychol Rev. 2020;76:101828.

Brown L, Kuss DJ. Fear of missing out, mental wellbeing, and social connectedness: a seven-day social media abstinence trial. Int J Environ Res Public Health. 2020;17(12):4566.

Lambert J, Barnstable G, Minter E, Cooper J, McEwan D. Taking a one-week break from social media improves well-being, depression, and anxiety: a randomized controlled trial. Cyberpsychol Behav Soc Netw. 2022;25(5):287–93.

Vally Z, D’Souza CG. Abstinence from social media use, subjective well-being, stress, and loneliness. Perspect Psychiatr Care. 2019;55(4):752–9.

Brailovskaia J, Delveaux J, John J, Wicker V, Noveski A, Kim S, et al. Finding the “sweet spot” of smartphone use: Reduction or abstinence to increase well-being and healthy lifestyle?! An experimental intervention study. J Exp Psychol Appl. 2022. Cited 2022 Jun 1; Available from: https://psycnet.apa.org/fulltext/2022-50916-001.pdf .

Brailovskaia J, Meier-Faust J, Schillack H, Margraf J. A two-week gaming abstinence reduces Internet Gaming Disorder and improves mental health: an experimental longitudinal intervention study. Comput Hum Behav. 2022;1(134):107334.

Huang C. Time spent on social network sites and psychological well-being: a meta-analysis. Cyberpsychol Behav Soc Netw. 2017;20(6):346–54.

Perloff RM. Social media effects on young women’s body image concerns: theoretical perspectives and an agenda for research. Sex Roles. 2014;71(11):363–77.

Rozgonjuk D, Ignell J, Mech F, Rothermund E, Gündel H, Montag C. Smartphone and Instagram use, body dissatisfaction, and eating disorders: investigating the associations using self-report and tracked data. J Eat Disord. 2023;11(1):149.

Andsager JL. Research directions in social media and body image. Sex Roles. 2014;71(11):407–13.

Burnette CB, Kwitowski MA, Mazzeo SE. “I don’t need people to tell me I’m pretty on social media:” a qualitative study of social media and body image in early adolescent girls. Body Image. 2017;23:114–25.

Kannen C. SurveyCoder. 2020. Cited 2021 May 16. Available from: https://www.surveycoder.com/ .

Calogero RM, Thompson KJ. Gender and body image. In: Chrisler JC, McCreary DR, editors. Handbook of gender research in psychology. Springer; 2010. p. 153–84.

Chapter   Google Scholar  

Stevens MW, Dorstyn D, Delfabbro PH, King DL. Global prevalence of gaming disorder: a systematic review and meta-analysis. Aust N Z J Psychiatry. 2021;55(6):553–68.

Wegmann E, Oberst U, Stodt B, Brand M. Online-specific fear of missing out and Internet-use expectancies contribute to symptoms of Internet-communication disorder. Addict Behav Rep. 2017;1(5):33–42.

Riordan BC, Cody L, Flett JAM, Conner TS, Hunter J, Scarf D. The development of a single item FoMO (Fear of Missing Out) scale. Curr Psychol. 2020;39(4):1215–20.

Kwon M, Kim DJ, Cho H, Yang S. The Smartphone Addiction Scale: Development And Validation Of A Short Version For Adolescents. PLOS One. 2013;8(12):e83558.

Article   PubMed   PubMed Central   ADS   Google Scholar  

Haug S, Castro RP, Kwon M, Filler A, Kowatsch T, Schaub MP. Smartphone use and smartphone addiction among young people in Switzerland. J Behav Addict. 2015;4(4):299–307.

Kroenke K, Spitzer RL, Williams JBW, Löwe B. An ultra-brief screening scale for anxiety and depression: The PHQ–4. Psychosomatics. 2009;50(6):613–21.

PubMed   Google Scholar  

Hughes ME, Waite LJ, Hawkley LC, Cacioppo JT. A short scale for measuring loneliness in large surveys: results from two population-based studies. Res Aging. 2004;26(6):655–72.

Montag C, Schivinski B, Sariyska R, Kannen C, Demetrovics Z, Pontes HM. Psychopathological symptoms and gaming motives in disordered gaming—a psychometric comparison between the WHO and APA diagnostic frameworks. J Clin Med. 2019;8(10):1691.

Gardner RM, Jappe LM, Gardner L. Development and validation of a new figural drawing scale for body-image assessment: the BIAS-BD. J Clin Psychol. 2009;65(1):113–22.

Vossbeck-Elsebusch AN, Waldorf M, Legenbauer T, Bauer A, Cordes M, Vocks S. German version of the Multidimensional Body-Self Relations Questionnaire – Appearance Scales (MBSRQ-AS): Confirmatory factor analysis and validation. Body Image. 2014;11(3):191–200.

Ahorsu DK, Lin CY, Imani V, Saffari M, Griffiths MD, Pakpour AH. The fear of COVID-19 scale: development and initial validation. Int J Ment Health Addict. 2020;27:1–9.

Fatfouta R, Rogoza R. Psychometric Properties and Factor Structure of the German Version of the Fear of COVID-19 Scale. OMEGA - J Death Dying. 2021;13:00302228211062360.

Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International Physical Activity Questionnaire: 12-Country Reliability and Validity: Med Sci Sports Exerc. 2003;35(8):1381–95.

Breyer B, Bluemke M. Deutsche Version der Positive and Negative Affect Schedule PANAS (GESIS Panel). Zusammenstellung Sozialwissenschaftlicher Items Skalen ZIS. 2016. Cited 2022 Jun 1; Available from: http://zis.gesis.org/DoiId/zis242 .

Watson D, Clark LA, Tellegen A. Development and validation of brief measures of positive and negative affect: the PANAS scales. J Pers Soc Psychol. 1988;54(6):1063–70.

Article   CAS   PubMed   Google Scholar  

Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. 1983;24(4):385.

Schneider EE, Schönfelder S, Domke-Wolf M, Wessa M. Measuring stress in clinical and nonclinical subjects using a German adaptation of the Perceived Stress Scale. Int J Clin Health Psychol. 2020;20(2):173–81.

Diener E, Emmons RA, Larsen RJ, Griffin S. The Satisfaction With Life Scale. J Pers Assess. 1985;49(1):71–5.

Janke S, Glöckner-Rist A. Deutsche Version der Satisfaction with Life Scale (SWLS). Zusammenstellung Sozialwissenschaftlicher Items Skalen ZIS. 2012. Cited 2022 Jul 2; Available from: http://zis.gesis.org/DoiId/zis147 .

R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2022. Available from: https://www.R-project.org/ .

Grimm KJ, Ram N, Estabrook R. Growth modeling: structural equation and multilevel modeling approaches. New York, NY: Guilford Press; 2017. 537 p. (Methodology in the social sciences).

Bates D, Mächler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models Using lme4. J Stat Softw. 2015.;67(1). Cited 2023 Jul 23. Available from: http://www.jstatsoft.org/v67/i01/ .

Kuznetsova A, Brockhoff PB, Christensen RHB. lmerTest Package: Tests in Linear Mixed Effects Models. J Stat Softw. 2017;82(13). Cited 2023 Jul 23. Available from: http://www.jstatsoft.org/v82/i13/ .

Sullivan BM, George AM. The association of motives with problematic smartphone use: A systematic review. Cyberpsychology J Psychosoc Res Cyberspace. 2023;17(1). Cited 2023 Jul 15. Available from: https://cyberpsychology.eu/article/view/20905 .

Zhang KZK, Chen C, Lee MKO. Understanding the role of motives in smartphone addiction. In Proceeding of the 19th Pacific Asia Conference on Information Systems (PACIS 2014) Pacific Asia Conference on Information Systems. https://aisel.aisnet.org/pacis2014/131 .

Elhai JD, Levine JC, O’Brien KD, Armour C. Distress tolerance and mindfulness mediate relations between depression and anxiety sensitivity with problematic smartphone use. Comput Hum Behav. 2018;84:477–84.

Elhai JD, Yang H, Fang J, Bai X, Hall BJ. Depression and anxiety symptoms are related to problematic smartphone use severity in Chinese young adults: Fear of missing out as a mediator. Addict Behav. 2020;101:105962.

Wang Y, Yang H, Montag C, Elhai JD. Boredom proneness and rumination mediate relationships between depression and anxiety with problematic smartphone use severity. Curr Psychol. 2020; Cited 2021 Mar 9. https://doi.org/10.1007/s12144-020-01052-0 .

Elhai JD, Vasquez JK, Lustgarten SD, Levine JC, Hall BJ. Proneness to boredom mediates relationships between problematic smartphone use with depression and anxiety severity. Soc Sci Comput Rev. 2018;36(6):707–20.

Rozgonjuk D, Sindermann C, Elhai JD, Montag C. Individual differences in Fear of Missing Out (FoMO): Age, gender, and the Big Five personality trait domains, facets, and items. Personal Individ Differ. 2021;171:110546.

Añez E, Fornieles-Deu A, Fauquet-Ars J, López-Guimerà G, Puntí-Vidal J, Sánchez-Carracedo D. Body image dissatisfaction, physical activity and screen-time in Spanish adolescents. J Health Psychol. 2018;23(1):36–47.

Hrafnkelsdottir SM, Brychta RJ, Rognvaldsdottir V, Chen KY, Johannsson E, Guðmundsdottir SL, et al. Screen time and body image in Icelandic adolescents: sex-specific cross-sectional and longitudinal associations. Int J Environ Res Public Health. 2022;19(3):1308.

Tang L, Rifas-Shiman SL, Field AE, Austin SB, Haines J. Self-reported total screen time and viewing modes are associated with body dissatisfaction, disordered eating, and cosmetic surgery intentions among young adults. Nutrients. 2022;14(10):2027.

Wang HY, Cheng C. The associations between gaming motivation and internet gaming disorder: systematic review and meta-analysis. JMIR Ment Health. 2022;9(2):e23700.

de Hesselle LC, Rozgonjuk D, Sindermann C, Pontes HM, Montag C. The associations between Big Five personality traits, gaming motives, and self-reported time spent gaming. Personal Individ Differ. 2020;6:110483.

Elhai JD, Gallinari EF, Rozgonjuk D, Yang H. Depression, anxiety and fear of missing out as correlates of social, non-social and problematic smartphone use. Addict Behav. 2020;105:106335.

Anastasi A. The nature of psychological “traits.” Psychol Rev. 1948;55(3):127–38.

Katz E, Blumler JG, Gurevitch M. Uses and Gratifications Research. Public Opin Q. 1973;37(4):509–23.

Whiting A, Williams D. Why people use social media: a uses and gratifications approach. Qual Mark Res Int J. 2013;16(4):362–9.

Download references

Acknowledgements

Not applicable.

Open Access funding enabled and organized by Projekt DEAL.

Author information

Authors and affiliations.

Department of Molecular Psychology, Institute of Psychology and Education, Ulm University, Helmholtzstraße 8/1, 89081, Ulm, Germany

Lea C. de Hesselle & Christian Montag

You can also search for this author in PubMed   Google Scholar

Contributions

LdH: Writing – Original Draft, Conceptualization, Formal Analysis, Data Curation. CM: Writing – Review & Editing, Investigation, Supervision.

Corresponding author

Correspondence to Lea C. de Hesselle .

Ethics declarations

Ethics approval and consent to participate.

The present study was approved by the university’s ethics committee (Ethics committee of University of Ulm) under application number 252/22 and all methods were carried out in accordance with relevant guidelines.

Informed consent was obtained from all participants in the study at the baseline assessment.

Consent for publication

Competing interests.

Dr. Montag reports no conflict of interest. However, for reasons of transparency Dr. Montag mentions that he has received (to Ulm University and earlier University of Bonn) grants from agencies such as the German Research Foundation (DFG). Dr. Montag has performed grant reviews for several agencies; has edited journal sections and articles; has given academic lectures in clinical or scientific venues or companies; and has generated books or book chapters for publishers of mental health texts. For some of these activities he received royalties, but never from gaming or social media companies. Dr. Montag mentions that he was part of a discussion circle (Digitalität und Verantwortung: https://about.fb.com/de/news/h/gespraechskreis-digitalitaet-und-verantwortung/ ) debating ethical questions linked to social media, digitalization and society/democracy at Facebook. In this context, he received no salary for his activities. Finally, he mentions that he currently functions as independent scientist on the scientific advisory board of the Nymphenburg group (Munich, Germany). This activity is financially compensated. Moreover, he is on the scientific advisory board of Applied Cognition (Redwood City, CA, USA), an activity which is also compensated.

Lea C. de Hesselle reports no conflict of interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary material 1., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

de Hesselle, L.C., Montag, C. Effects of a 14-day social media abstinence on mental health and well-being: results from an experimental study. BMC Psychol 12 , 141 (2024). https://doi.org/10.1186/s40359-024-01611-1

Download citation

Received : 25 October 2023

Accepted : 20 February 2024

Published : 13 March 2024

DOI : https://doi.org/10.1186/s40359-024-01611-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Social media

BMC Psychology

ISSN: 2050-7283

• General enquiries: [email protected]

Don't miss tomorrow's social media industry news

Let Social Media Today's free newsletter keep you informed, straight from your inbox.

Instagram Experiments With Chat Widgets to Prompt More Engagement

Instagram’s testing out another way to fuel more engagement in DMs, this time via chat widgets, which would enable you to add additional tools and indicators into your chat flow.

As you can see in this example , posted by app researcher Alessandro Paluzzi , Instagram’s seemingly developing new “Chat Widgets,” with a range of add-ons for your discussion, which could help to add more context, drive more engagement, etc.

The current chat widgets listed (in development, not in public testing as yet) are:

• Countdown widget – Create a countdown in the chat
• Timezone widget – Display the local time of each chat participant
• Pinned content widget – Quickly access pinned content

You can see how each of these could play a part in driving more engagement, by adding another element up front within the chat, so that you can more easily prompt one another over these informational tools.

It could also be of benefit for brand engagement. If the feature is made available to brands, you could, for example, prompt customers with an offer, with a timer attached to accept. Having local time displayed would also be of benefit, to ensure that you’re responding at a reasonable hour.

Private sharing, primarily in DMs, has become a much bigger element of the broader IG experience, with Instagram chief Adam Mosseri repeatedly noting that :

“ Friends now post a lot more to stories, and send a lot more DMs, than they post to Feed.”

That’s indicative of the broader shift in social media engagement towards more enclosed sharing, as opposed to posting publicly, and Instagram’s been working to feed into this with new private sharing elements, including its increasingly popular inbox Notes element .

As such, chat widgets makes sense, and would be another element to enhance this focus.

There’s no word from Instagram as to a possible release of this element, nor a full list of widgets that would be available, but we’ll keep you updated on any progress.

Social Media Today news delivered to your inbox

Get the free daily newsletter read by industry experts

• Select user consent: By signing up to receive our newsletter, you agree to our Terms of Use and Privacy Policy . You can unsubscribe at anytime.

Editors' picks

3 Steps for Establishing a Core Marketing Message

The key to maximizing a social media marketing strategy is to ground the approach in purpose.

The Complete Elon Musk–Twitter Saga

For months, we’ve been tracking Elon Musk’s $44 billion deal with Twitter. Now that the deal is done, we watch as the Musk-Twitter era begins.

Company Announcements

• Meta’s Apps Continue To Dominate the Download Charts By Andrew Hutchinson
• Meta Brings Its AI Chatbot to Indian Users By Andrew Hutchinson
• Instagram Experiments With Chat Widgets to Prompt More Engagement By Andrew Hutchinson
• Instagram Adds Music for Carousel Posts With Video By Andrew Hutchinson

The Social Media Well-Being Experiment

Teen researchers Destinee Ramos and Isabel Yoblonski have received IRB approval and are now recruiting participants for their nationwide study on teen social media habits and mental and physical health.

There have been a lot of developments in the Social Media Detox Experiment since our last post about the pilot study on social media use and adolescent girls’ health.

One thing that has stayed the same, however,  is the tenacity of Destinee Ramos and Isabel Yoblonski.  These two have shown no signs of stopping since their media appearances last fall. With growing academic and public interest in screen time and teen well-being, Destinee and Isabel have turned to science to explore the relationship between social media use and teenage mental and physical health. Their aim is to introduce evidence-based recommendations for healthy usage to both the scientific community and the general public. 

Instagram Funding

Amidst the attention surrounding their pilot study, the girls have caught the eye of one social media giant in particular - Instagram. Although the platform has been heavily criticized for its effect on teens, it has decided to address the issue through research. In an effort to promote adolescent mental health and protect teens,  Instagram has provided a substantial donation to Destinee and Isabel’s GoFundMe campaign. After all, who better to help conduct the research than the ones most impacted by it?

IRB Approval

Isabel and Destinee worked assiduously to get IRB approval from WCG IRB with guidance from mentors from Harvard University, Drs. Andrew Ahn and Emily Weinstein,  along with UC Berkeley’s Dr. Azure Grant. 

The IRB process is arduous for anyone, let alone teenagers.  But now they are ready for the exciting part –actually getting their study off the ground. 

Study Recruitment

They are currently recruiting participants for the first-ever nationwide study launched by teenagers using innovative technologies. Since the use of wearables enables them to conduct the study remotely from end to end, they are in a unique position to recruit from all over the United States. They’re recruiting 50 teen girls throughout the U.S. between the ages of 15-17.

Study Protocol

Using Garmin smartwatches, participants’ heart rate, stress, heart rate variability (HRV) , and sleep quality will be recorded.  Data will be collected through Labfront  and the Garmin app 24/7 over a two-week period. Participants will be randomly assigned to complete either a full disengagement from social media for 4 days  (Intervention 1) or a partial disengagement from social media with restricted access from 5 PM- 8 AM (Intervention 2) for 4 days. Daily digital questionnaires will also be completed to compare self-reported and objective data.  ‍

We hope this study can help in answering questions about optimizing social media habits in ways that support mental and physical health. Keep checking our blog for more updates! 

Alix doubles as the marketing and pun specialist at Labfront. She usually operates quietly behind the scenes, but give her a karaoke mic and all bets are off.

Subscribe to Human Science

MIT Technology Review

• Newsletters

Why artists are becoming less scared of AI

As people tinker and experiment with it, we’re gaining a clearer understanding of its limitations in creative fields.

• Melissa Heikkilä archive page

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here .

Knock, knock. 

Who’s there? 

An AI with generic jokes. Researchers from Google DeepMind asked 20 professional comedians to use popular AI language models to write jokes and comedy performances. Their results were mixed. 

The comedians said that the tools were useful in helping them produce an initial “vomit draft” that they could iterate on, and helped them structure their routines. But the AI was not able to produce anything that was original, stimulating, or, crucially, funny. My colleague Rhiannon Williams  has the full story .

As Tuhin Chakrabarty, a computer science researcher at Columbia University who specializes in AI and creativity, told Rhiannon, humor often relies on being surprising and incongruous. Creative writing requires its creator to deviate from the norm, whereas LLMs can only mimic it.

And that is becoming pretty clear in the way artists are approaching AI today. I’ve just come back from Hamburg, which hosted one of the  largest events for creatives in Europe , and the message I got from those I spoke to was that AI is too glitchy and unreliable to fully replace humans and is best used instead as a tool to augment human creativity. 

Right now, we are in a moment where we are deciding how much creative power we are comfortable giving AI companies and tools. After the boom first started in 2022, when DALL-E 2 and Stable Diffusion first entered the scene, many artists raised concerns that AI companies were scraping their copyrighted work without consent or compensation. Tech companies argue that anything on the public internet falls under fair use, a legal doctrine that allows the reuse of copyrighted-protected material in certain circumstances. Artists, writers, image companies, and the New York Times have filed lawsuits against these companies, and it will likely take years until we have a clear-cut answer as to who is right. 

Meanwhile, the court of public opinion has shifted a lot in the past two years. Artists I have interviewed recently say they were harassed and ridiculed for protesting AI companies’ data-scraping practices two years ago. Now, the general public is more aware of the harms associated with AI. In just two years, the public has gone from being blown away by AI-generated images to sharing viral social media posts about how to opt out of AI scraping—a concept that was alien to most laypeople until very recently. Companies have benefited from this shift too. Adobe has been successful in pitching its  AI offerings  as an “ethical” way to use the technology without having to worry about copyright infringement. 

There are also several grassroots efforts to shift the power structures of AI and give artists more agency over their data. I’ve written about  Nightshade , a tool created by researchers at the University of Chicago, which lets users add an invisible poison attack to their images so that they break AI models when scraped. The same team is behind Glaze, a tool that lets artists mask their personal style from AI copycats. Glaze has been integrated into Cara, a buzzy new art portfolio site and social media platform, which has seen a surge of interest from artists. Cara pitches itself as a platform for art created by people; it filters out AI-generated content. It got nearly a million new users in a few days. 

This all should be reassuring news for any creative people worried that they could lose their job to a computer program. And the DeepMind study is a great example of how AI can actually be helpful for creatives. It can take on some of the boring, mundane, formulaic aspects of the creative process, but it can’t replace the magic and originality that humans bring. AI models are limited to their training data and will forever only reflect the zeitgeist at the moment of their training. That gets old pretty quickly.

Now read the rest of The Algorithm

Deeper learning, apple is promising personalized ai in a private cloud. here’s how that will work..

Last week, Apple unveiled its vision for supercharging its product lineup with artificial intelligence. The key feature, which will run across virtually all of its product line, is Apple Intelligence, a suite of AI-based capabilities that promises to deliver personalized AI services while keeping sensitive data secure. 

Why this matters:  Apple says its privacy-focused system will first attempt to fulfill AI tasks locally on the device itself. If any data is exchanged with cloud services, it will be encrypted and then deleted afterward. It’s a pitch that offers an implicit contrast with the likes of Alphabet, Amazon, or Meta, which collect and store enormous amounts of personal data.  Read more from James O’Donnell here . 

Bits and Bytes

How to opt out of Meta’s AI training If you post or interact with chatbots on Facebook, Instagram, Threads, or WhatsApp, Meta can use your data to train its generative AI models. Even if you don’t use any of Meta’s platforms, it can still scrape data such as photos of you if someone else posts them. Here’s our quick guide on how to opt out. ( MIT Technology Review ) 

Microsoft’s Satya Nadella is building an AI empire Nadella is going all in on AI. His $13 billion investment in OpenAI was just the beginning. Microsoft has become an “the world’s most aggressive amasser of AI talent, tools, and technology” and has started building an in-house OpenAI competitor. ( The Wall Street Journal )

OpenAI has hired an army of lobbyists As countries around the world mull AI legislation, OpenAI is on a lobbyist hiring spree to protect its interests. The AI company has expanded its global affairs team from three lobbyists at the start of 2023 to 35 and intends to have up to 50 by the end of this year. ( Financial Times )  

UK rolls out Amazon-powered emotion recognition AI cameras on trains People traveling through some of the UK’s biggest train stations have likely had their faces scanned by Amazon software without their knowledge during an AI trial. London stations such as Euston and Waterloo have tested CCTV cameras with AI to reduce crime and detect people’s emotions. Emotion recognition technology is extremely controversial. Experts say it is unreliable and simply does not work.  ( Wired ) 

Clearview AI used your face. Now you may get a stake in the company. The facial recognition company, which has been under fire for scraping images of people’s faces from the web and social media without their permission, has agreed to an unusual settlement in a class action against it. Instead of paying cash, it is offering a 23% stake in the company for Americans whose faces are in its data sets. ( The New York Times ) 

Artificial intelligence

How to opt out of meta’s ai training.

Your posts are a gold mine, especially as companies start to run out of AI training data.

Why does AI hallucinate?

The tendency to make things up is holding chatbots back. But that’s just what they do.

• Will Douglas Heaven archive page

Apple’s first big salvo in the AI wars makes a bet that people will care about data privacy when automating tasks.

• James O'Donnell archive page

This AI-powered “black box” could make surgery safer

A new smart monitoring system could help doctors avoid mistakes—but it’s also alarming some surgeons and leading to sabotage.

• Simar Bajaj archive page

Stay connected

Get the latest updates from mit technology review.

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at [email protected] with a list of newsletters you’d like to receive.

Social Media and Job Market Success: A Field Experiment on Twitter

80 Pages Posted: 20 May 2024

University of Michigan, School of Information

University of Michigan at Ann Arbor - School of Information

Alvin E. Roth

National Bureau of Economic Research (NBER); Dept. of Economics, Stanford University

Date Written: May 20, 2024

We conducted a field experiment on Twitter to examine the impact of social media promotion on job market outcomes in economics. Half of the 519 job market papers tweeted from our research account were randomly assigned to be quote-tweeted by prominent economists. Papers assigned to be quote-tweeted received 442% more views and 303% more likes. Moreover, candidates in the treatment group received one additional flyout, with women receiving 0.9 more job offers. These findings suggest that social media promotion can improve the visibility and success of job market candidates, especially for underrepresented groups in economics such as women.

Keywords: social media, academic job market, field experiment

JEL Classification: D47, C78, C92, D82

Suggested Citation: Suggested Citation

University of Michigan, School of Information ( email )

304 West Hall 550 East University Ann Arbor, MI 48109-1092 United States 48109 (Fax)

HOME PAGE: http://jingyiqiu.com

University of Michigan at Ann Arbor - School of Information ( email )

304 West Hall 550 East University Ann Arbor, MI 48109-1092 United States

Alain Cohn (Contact Author)

National bureau of economic research (nber).

1050 Massachusetts Avenue Cambridge, MA 02138 United States

Dept. of Economics, Stanford University ( email )

Landau Economics Building 579 Serra Mall STANFORD, CA 94305-6072 United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics, related ejournals, behavioral & experimental economics ejournal.

Subscribe to this fee journal for more curated articles on this topic

Randomized Social Experiments eJournal

Advertising, marketing & strategic communication ejournal, industrial & organizational psychology ejournal, information policy, ethics, access & use ejournal, psychology research methods ejournal, social media studies ejournal.

Subscribe to this free journal for more curated articles on this topic