body of research paper about cyberbullying

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The nature of cyberbullying, the impact of cyberbullying on emotional health and well-being, technological solutions, asking adults for help, cyberbullying and its impact on young people's emotional health and well-being.

Published online by Cambridge University Press:  02 January 2018

The upsurge of cyberbullying is a frequent cause of emotional disturbance in children and young people. The situation is complicated by the fact that these interpersonal safety issues are actually generated by the peer group and in contexts that are difficult for adults to control. This article examines the effectiveness of common responses to cyberbullying.

Whatever the value of technological tools for tackling cyberbullying, we cannot avoid the fact that this is an interpersonal problem grounded in a social context.

Practitioners should build on existing knowledge about preventing and reducing face-to-face bullying while taking account of the distinctive nature of cyberbullying. Furthermore, it is essential to take account of the values that young people are learning in society and at school.

Traditional face-to-face bullying has long been identified as a risk factor for the social and emotional adjustment of perpetrators, targets and bully victims during childhood and adolescence; Reference Almeida, Caurcel and Machado 1 - Reference Sourander, Brunstein, Ikomen, Lindroos, Luntamo and Koskelainen 6 bystanders are also known to be negatively affected. Reference Ahmed, Österman and Björkqvist 7 - Reference Salmivalli 9 The emergence of cyberbullying indicates that perpetrators have turned their attention to technology (including mobile telephones and the internet) as a powerful means of exerting their power and control over others. Reference Smith, Mahdavi, Carvalho, Fisher, Russell and Tippett 10 Cyberbullies have the power to reach their targets at any time of the day or night.

Cyberbullying takes a number of forms, to include:

• flaming: electronic transmission of angry or rude messages;

• harassment: repeatedly sending insulting or threatening messages;

• cyberstalking: threats of harm or intimidation;

• denigration: put-downs, spreading cruel rumours;

• masquerading: pretending to be someone else and sharing information to damage a person’s reputation;

• outing: revealing personal information about a person which was shared in confidence;

• exclusion: maliciously leaving a person out of a group online, such as a chat line or a game, ganging up on one individual. Reference Schenk and Fremouw 11

Cyberbullying often occurs in the context of relationship difficulties, such as the break-up of a friendship or romance, envy of a peer’s success, or in the context of prejudiced intolerance of particular groups on the grounds of gender, ethnicity, sexual orientation or disability. Reference Hoff and Mitchell 12

A survey of 23 420 children and young people across Europe found that, although the vast majority were never cyberbullied, 5% were being cyberbullied more than once a week, 4% once or twice a month and 10% less often. Reference Livingstone, Haddon, Anke Görzig and Ólafsson 13 Many studies indicate a significant overlap between traditional bullying and cyberbullying. Reference Perren, Dooley, Shaw and Cross 5 , Reference Sourander, Brunstein, Ikomen, Lindroos, Luntamo and Koskelainen 6 , Reference Kowalski and Limber 14 , Reference Ybarra and Mitchell 15 However, a note of caution is needed when interpreting the frequency and prevalence of cyberbullying. As yet, there is no uniform agreement on its definition and researchers differ in the ways they gather their data, with some, for example, asking participants whether they have ‘ever’ been cyberbullied and others being more specific, for example, ‘in the past 30 days’.

Research consistently identifies the consequences of bullying for the emotional health of children and young people. Victims experience lack of acceptance in their peer groups, which results in loneliness and social isolation. The young person’s consequent social withdrawal is likely to lead to low self-esteem and depression. Bullies too are at risk. They are more likely than non-bullies to engage in a range of maladaptive and antisocial behaviours, and they are at risk of alcohol and drugs dependency; like victims, they have an increased risk of depression and suicidal ideation. Studies among children Reference Escobar, Fernandez-Baen, Miranda, Trianes and Cowie 2 - Reference Kaltiala-Heino, Rimpalä, Rantanen and Rimpalä 4 , Reference Kumpulainen, Rasanen and Henttonen 16 and adolescents Reference Salmivalli, Lappalainen and Lagerspetz 17 , Reference Sourander, Helstela, Helenius and Piha 18 indicate moderate to strong relationships between being nominated by peers as a bully or a victim at different time points, suggesting a process of continuity. The effects of being bullied at school can persist into young adulthood. Reference Isaacs, Hodges and Salmivalli 19 , Reference Lappalainen, Meriläinen, Puhakka and Sinkkonen 20

Studies demonstrate that most young people who are cyberbullied are already being bullied by traditional, face-to-face methods. Reference Sourander, Brunstein, Ikomen, Lindroos, Luntamo and Koskelainen 6 , Reference Dooley, Pyzalski and Cross 21 - Reference Riebel, Jaeger and Fischer 23 Cyberbullying can extend into the target’s life at all times of the day and night and there is evidence for additional risks to the targets of cyberbullying, including damage to self-esteem, academic achievement and emotional well-being. For example, Schenk & Fremouw Reference Schenk and Fremouw 11 found that college student victims of cyberbullying scored higher than matched controls on measures of depression, anxiety, phobic anxiety and paranoia. Studies of school-age cyber victims indicate heightened risk of depression, Reference Perren, Dooley, Shaw and Cross 5 , Reference Gradinger, Strohmeier and Spiel 22 , Reference Juvonen and Gross 24 of psychosomatic symptoms such as headaches, abdominal pain and sleeplessness Reference Sourander, Brunstein, Ikomen, Lindroos, Luntamo and Koskelainen 6 and of behavioural difficulties including alcohol consumption. Reference Mitchell, Ybarra and Finkelhor 25 As found in studies of face-to-face bullying, cyber victims report feeling unsafe and isolated, both at school and at home. Similarly, cyberbullies report a range of social and emotional difficulties, including feeling unsafe at school, perceptions of being unsupported by school staff and a high incidence of headaches. Like traditional bullies, they too are engaged in a range of other antisocial behaviours, conduct disorders, and alcohol and drug misuse. Reference Sourander, Brunstein, Ikomen, Lindroos, Luntamo and Koskelainen 6 , Reference Hinduja and Patchin 26

The most fundamental way of dealing with cyberbullying is to attempt to prevent it in the first place, through whole-school e-safety policies Reference Campbell 27 - Reference Stacey 29 and through exposure to the wide range of informative websites that abound (e.g. UK Council for Child Internet Safety (UKCCIS; www.education.gov.uk/ukccis ), ChildLine ( www.childline.org.uk )). Many schools now train pupils in e-safety and ‘netiquette’ to equip them with the critical tools that they will need to understand the complexity of the digital world and become aware of its risks as well as its benefits. Techniques include blocking bullying behaviour online or creating panic buttons for cyber victims to use when under threat. Price & Dalgleish Reference Price and Dalgleish 30 found that blocking was considered as a most helpful online action by cyber victims and a number of other studies have additionally found that deleting nasty messages and stopping use of the internet were effective strategies. Reference Livingstone, Haddon, Anke Görzig and Ólafsson 13 , Reference Kowalski and Limber 14 , Reference Juvonen and Gross 24 However, recent research by Kumazaki et al Reference Kumazaki, Kanae, Katsura, Akira and Megumi 31 found that training young people in netiquette did not significantly reduce or prevent cyberbullying. Clearly there is a need for further research to evaluate the effectiveness of different types of technological intervention.

Parents play an important role in prevention by banning websites and setting age-appropriate limits of using the computer and internet. Reference Kowalski and Limber 14 Poor parental monitoring is consistently associated with a higher risk for young people to be involved in both traditional and cyberbullying, whether as perpetrator or target. Reference Ybarra and Mitchell 15 However, adults may be less effective in dealing with cyberbullying once it has occurred. Most studies confirm that it is essential to tell someone about the cyberbullying rather than suffer in silence and many students report that they would ask their parents for help in dealing with a cyberbullying incident. Reference Smith, Mahdavi, Carvalho, Fisher, Russell and Tippett 10 , Reference Stacey 29 , Reference Aricak, Siyahhan, Uzunhasanoglu, Saribeyoglu, Ciplak and Yilmaz 32 On the other hand, some adolescents recommend not consulting adults because they fear loss of privileges (e.g. having and using mobile telephones and their own internet access), and because they fear that their parents would simply advise them to ignore the situation or that they would not be able to help them as they are not accustomed to cyberspace. Reference Smith, Mahdavi, Carvalho, Fisher, Russell and Tippett 10 , Reference Hoff and Mitchell 12 , Reference Kowalski and Limber 14 , Reference Stacey 29 In a web-based survey of 12- to 17-year-olds, of whom most had experienced at least one cyberbullying incident in the past year, Juvonen & Gross Reference Juvonen and Gross 24 found that 90% of the victims did not tell their parents about their experiences and 50% of them justified it with ‘I need to learn to deal with it myself’.

Students also have a rather negative and critical attitude to teachers’ support and a large percentage consider telling a teacher or the school principal as rather ineffective. Reference Aricak, Siyahhan, Uzunhasanoglu, Saribeyoglu, Ciplak and Yilmaz 32 , Reference DiBasilio 33 Although 17% of students reported to a teacher after a cyberbullying incident, in 70% of the cases the school did not react to it. Reference Hoff and Mitchell 12

Involving peers

Young people are more likely to find it helpful to confide in peers. Reference Livingstone, Haddon, Anke Görzig and Ólafsson 13 , Reference Price and Dalgleish 30 , Reference DiBasilio 33 Additionally, it is essential to take account of the bystanders who usually play a critical role as audience to the cyberbullying in a range of participant roles, and who have the potential to be mobilised to take action against cyberbullying. Reference Salmivalli 9 , Reference Cowie 34 For example, a system of young cyber mentors, trained to monitor websites and offer emotional support to cyber victims, was positively evaluated by adolescents. Reference Banerjee, Robinson and Smalley 35 Similarly, DiBasilio Reference DiBasilio 33 showed that peer leaders in school played a part in prevention of cyberbullying by creating bullying awareness in the school, developing leadership skills among students, establishing bullying intervention practices and team-building initiatives in the student community, and encouraging students to behave proactively as bystanders. This intervention successfully led to a decline in cyberbullying, in that the number of students who participated in electronic bullying decreased, while students’ understanding of bullying widened.

Although recommended strategies for coping with cyberbullying abound, there remains a lack of evidence about what works best and in what circumstances in counteracting its negative effects. However, it would appear that if we are to solve the problem of cyberbullying, we must also understand the networks and social groups where this type of abuse occurs, including the importance that digital worlds play in the emotional lives of young people today, and the disturbing fact that cyber victims can be targeted at any time and wherever they are, so increasing their vulnerability.

There are some implications for professionals working with children and young people. Punitive methods tend on the whole not to be effective in reducing cyberbullying. In fact, as Shariff & Strong-Wilson Reference Shariff, Strong-Wilson and Kincheloe 36 found, zero-tolerance approaches are more likely to criminalise young people and add a burden to the criminal justice system. Interventions that work with peer-group relationships and with young people’s value systems have a greater likelihood of success. Professionals also need to focus on the values that are held within their organisations, in particular with regard to tolerance, acceptance and compassion for those in distress. The ethos of the schools where children and young people spend so much of their time is critical. Engagement with school is strongly linked to the development of positive relationships with adults and peers in an environment where care, respect and support are valued and where there is an emphasis on community. As Batson et al Reference Batson, Ahmad, Lishner, Tsang, Snyder and Lopez 37 argue, empathy-based socialisation practices encourage perspective-taking and enhance prosocial behaviour, leading to more satisfying relationships and greater tolerance of stigmatised outsider groups. This is particularly relevant to the discussion since researchers have consistently found that high-quality friendship is a protective factor against mental health difficulties among bullied children. Reference Skrzypiec, Slee, Askell-Williams and Lawson 38

Finally, research indicates the importance of tackling bullying early before it escalates into something much more serious. This affirms the need for schools to establish a whole-school approach with a range of systems and interventions in place for dealing with all forms of bullying and social exclusion. External controls have their place, but we also need to remember the interpersonal nature of cyberbullying. This suggests that action against cyberbullying should be part of a much wider concern within schools about the creation of an environment where relationships are valued and where conflicts are seen to be resolved in the spirit of justice and fairness.

Acknowledgement

I am grateful to the COST ACTION IS0801 for its support in preparing this article ( https://sites.google.com/site/costis0801 ).

Declaration of interest

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• Volume 37, Issue 5
• Helen Cowie (a1)
• DOI: https://doi.org/10.1192/pb.bp.112.040840

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• Published: 29 April 2024

Problematic social media use mediates the effect of cyberbullying victimisation on psychosomatic complaints in adolescents

• Prince Peprah 1 , 2 ,
• Michael Safo Oduro 3 ,
• Godfred Atta-Osei 4 ,
• Isaac Yeboah Addo 5 , 6 ,
• Anthony Kwame Morgan 7 &
• Razak M. Gyasi 8 , 9  

Scientific Reports volume  14 , Article number:  9773 ( 2024 ) Cite this article

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• Public health
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Adolescent psychosomatic complaints remain a public health issue globally. Studies suggest that cyberbullying victimisation, particularly on social media, could heighten the risk of psychosomatic complaints. However, the mechanisms underlying the associations between cyberbullying victimisation and psychosomatic complaints remain unclear. This cross-cultural study examines the mediating effect of problematic social media use (PSMU) on the association between cyberbullying victimisation and psychosomatic complaints among adolescents in high income countries. We analysed data on adolescents aged 11–16.5 years (weighted N = 142,298) in 35 countries participating in the 2018 Health Behaviour in School-aged Children (HBSC) study. Path analysis using bootstrapping technique tested the hypothesised mediating role of PSMU. Results from the sequential binary mixed effects logit models showed that adolescents who were victims of cyberbullying were 2.39 times significantly more likely to report psychosomatic complaints than those who never experienced cyberbullying (AOR = 2.39; 95%CI = 2.29, 2.49). PSMU partially mediated the association between cyberbullying victimisation and psychosomatic complaints accounting for 12% ( \(\beta\)  = 0.01162, 95%CI = 0.0110, 0.0120) of the total effect. Additional analysis revealed a moderation effect of PSMU on the association between cyberbullying victimisation and psychosomatic complaints. Our findings suggest that while cyberbullying victimisation substantially influences psychosomatic complaints, the association is partially explained by PSMU. Policy and public health interventions for cyberbullying-related psychosomatic complaints in adolescents should target safe social media use.

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Introduction.

Adolescence is noted to be a critical developmental stage, with many problems, including loneliness 1 , poor friendships, an adverse class climate, school pressure 2 , suicidal ideation and attempts, and psychosomatic complaints 3 . Psychosomatic complaint is a combination of physical ailments (i.e., headaches, stomach aches, fatigue, and muscle pain) caused or exacerbated by psychological factors such as stress, irritability, anxiety, or emotional distress 4 , 5 . Psychosomatic complaints are common among adolescents, and recent estimates indicate that the global prevalence of psychosomatic complaints ranges between 10 and 50% 6 . Also, an increase in self-reported psychosomatic complaints and related mental health complaints have been reported in adolescents from high-income countries 7 , 8 . The high prevalence of psychosomatic complaints is of concern as psychosomatic complaints have severe implications for multiple detrimental health outcomes, healthcare expenditure, and quality of life of young people 9 . Thus, it is of utmost importance to identify the proximate risk factors for psychosomatic complaints among young people to aid in developing targeted interventions to reduce the incidence of psychosomatic complaints, mainly in high-income countries.

While extant research has identified risk factors for psychosomatic complaints, including malnutrition, low physical activity, and poor parental guidance 10 , 11 , 12 , one understudied but potentially important risk factor is cyberbullying victimisation. Cyberbullying victimisation is an internet-based aggressive and intentional act of continually threatening, harassing, or embarrassing individuals who cannot defend themselves using electronic contact forms such as emails, text messages, images, and videos 13 , 14 . Indeed, being typical of interpersonal interactions, cyberbullying victimisation has shown a rising trend, particularly during adolescence 15 . International literature has shown the prevalence of cyberbullying victimisation to be between 12 and 72% among young people 14 , 16 . It may be hypothesised that cyberbullying victimisation potentially increases the risk of psychosomatic complaints through factors such as problematic social media use (PSMU) 17 , 18 . However, studies are needed to identify whether and the extent to which such factors mediate the potential association of cyberbullying victimisation with psychosomatic complaints among young people.

Given this background, the present study aimed to investigate the association between cyberbullying victmisation and psychosomatic complaints in 142,298 young people aged 11–16.5 years from 35 high-income countries. A further aim was to quantify how PSMU mediates the association between cyberbullying victimisation and psychosomatic complaints.

Cyberbullying victimisation and adolescents’ psychosomatic complaints

Research has consistently shown that cyberbullying victimisation significantly impacts adolescents’ mental health 19 . For example, Kowalski and Limber 20 found that cyberbullying victimisation is associated with increased levels of depression, anxiety, and social anxiety, as well as psychosomatic complaints, such as fatigue and muscle tension. Further, studies have shown that cyberbullying victimisation and perpetration can lead to a variety of physical, social, and mental health issues, including substance abuse and suicidal thoughts and attempts 21 , 22 , 23 , 24 . Furthermore, cyberbullying victimisation is strongly associated with suicidal thoughts and attempts, regardless of demographic factors like gender or age 21 , 25 . These findings underscore the urgent need for interventions that address the mental health consequences of cyberbullying, particularly for adolescents, who are most vulnerable to its harmful effects. The findings also suggest that cyberbullying might be a potential underlying predictor of higher psychosomatic disorders among adolescents. This present study, therefore, hypothesises that H1: there is a statistically significant association between cyberbullying victimisation (X) and psychosomatic complaints (Y) (total effect).

The role of adolescents’ PSMU

Problematic Social Media Use (PSMU), a subtype of problematic internet use, refers to the uncontrolled, compulsive or excessive engagement with social media platforms such as Facebook and Twitter, characterised by addictive behaviours like mood alteration, withdrawal symptoms, and interpersonal conflicts. This pattern of social media usage can result in functional impairments and adverse outcomes 26 . Scholars and professionals have shown great concern about the length of time adolescents spend on social media. Studies have observed that (early) adolescence could be a crucial and sensitive developmental stage in which adolescent users might be unable to avoid the harmful impacts of social media use 27 . According to current research, PSMU may increase adolescents’ exposure to cyberbullying victimisation, which can have severe consequences for their mental health 28 , 29 , 30 . Similarly, an association between PSMU and physical/somatic problems, as well as somatic disorders, has been established in many studies 31 , 32 . Hanprathet et al. 33 demonstrated the negative impact of problematic Facebook use on general health, including somatic symptoms, anxiety, insomnia, depression, and social dysfunction. According to Cerutti et al. 34 , adolescents with problematic social media usage have more somatic symptoms, such as stomach pain, headaches, sore muscles, and poor energy, than their counterparts. Hence, inadequate sleep may be associated with PSMU, harming both perceived physical and mental health 35 , 36 . Again, supporting the above evidence, the relationship between PSMU, well-being, and psychological issues have been highlighted in meta-analytic research and systematic reviews 27 , 31 , 37 , 38 . Thus, this study proposes the following hypothesis: H2: there is a specific indirect effect of cyberbullying victimisation (X) on psychosomatic complaints (Y) through PSMU (M1) (indirect effect a 1 b 1 ).

Study, sample, and procedures

This study used data from the 2018 Health Behaviour in School-aged Children (HBSC) survey conducted in 35 countries and regions across Europe and Canada during the 2017–2018 academic year 39 . The HBSC research team/network is an international alliance of researchers collaborating on a cross-national survey of school students. The HBSC collects data every four years on 11-, 13- and 15- year-old adolescent boys’ and girls’ health and well-being, social environments, and health behaviours. The sampling procedure for the 2018 survey followed international guidelines 40 , 41 . A systematic sampling method was used to identify schools in each region from the complete list of both public and private schools. Participants were recruited through a cluster sampling approach, using the school class as the primary sampling unit 42 . Some countries oversampled subpopulations (e.g., by geography and ethnicity), and standardised weights were created to ensure representativeness of the population of 11, 13, and 15 years 43 . Questionnaires were translated based on a standard procedure to allow comparability between the participating countries. Our analysis used data from 35 countries and regions with complete data on cyberbullying victimisation, PSMU, and psychosomatic complaints. The study complies with ethical standards in each country and follows ethical guidelines for research and data protection from the World Health Organisation and the Organisation for Economic Co-operation and Development. Depending on the country, active or passive consent was sought from parents or legal guardians and students which was checked by teachers to participate in the study. The survey was conducted anonymously and participation in the study was voluntary for schools and students. Schools, children and adolescents could refuse to participate or withdraw their consent until the day of the survey. Moreover, all participating students were free to cease filling out the questionnaire at any moment, or to answer only selected questions. More detailed information on the methodology of the HBSC study including ethics and data protection can be found elsewhere 44 , 45 .

Outcome variable: psychosomatic complaints

Psychosomatic complaints was assessed by one collective item asking students how often they had experienced the following complaints over the past six months: headache, stomach aches, feeling low, irritability or bad mood, feeling nervous, dizziness, abdominal pain, sleep difficulty, and backache. Response options included: about every day, more than once a week, about every week, about every month, and rarely or never. This scale has sufficient test–retest reliability and validity 46 , good internal consistency (Cronbach’s a = 0.82) 47 , and has been applied in several multiple country analyses 48 , 49 . The scale is predictive of emotional problems and suicidal ideation in adolescents 50 , 51 . For our analysis, the scale was dichotomised with two or more complaints several times a week or daily coded as having psychosomatic complaints 47 , 49 .

Exposure variable: Cyberbullying victimisation

Cyberbullying victimisation is the exposure variable in this study. Thus, the exposure variable pertains to only being a victim of cyberbullying and does not include perpetration of cyberbullying. Students were first asked to read and understand a short definition of cyberbullying victimisation. They were then asked how often they were bullied over the past two months (e.g., someone sending mean instant messages, emails, or text messages about you; wall postings; creating a website making fun of you; posting unflattering or inappropriate pictures of you online without your permission or sharing them with others). Responses included: “ I have not   been  cyberbullied”, “once or twice”, “two or three times a month”, “about once a week”, and “several times a week”. These were dichotomised into “never" or “once or more". This measure of bullying victimisation has been validated across multiple cultural settings 43 , 52 , 53 , 54 .

Mediating variable

Problematic social media use (PSMU) was assessed with the Social Media Disorder Scale (Cronbach’s a = 0.89) 55 . The scale contains nine dichotomous (yes/no) items describing addiction-like symptoms, including preoccupation with social media, dissatisfaction about lack of time for social media, feeling bad when not using social media, trying but failing to spend less time using social media, neglecting other duties to use social media, frequent arguments over social media, lying to parents or friends about social media use, using social media to escape from negative feelings, and having a severe conflict with family over social media use. In this study, the endorsement of six or more items indicated PSMU as evidence suggests that a threshold of six or more is an indicative of PSMU 54 , 56 . This scale has been used across cultural contexts 43 , 52 , 54 .

Informed by previous studies 43 , 54 , 57 , the analysis controlled for theoretically relevant confounders, including sex (male/female) and age. Family affluence/socio-economic class was assessed using the Relative Family Affluence Scale, a validated six-item measure of material assets in the home, such as the number of vehicles, bedroom sharing, computer ownership, bathrooms at home, dishwashers at home, and family vacations) 56 , 58 . Finally, parental and peer support were measured using an eight item-measure 59 . Responses were recorded on a 7-point Likert scale (ranging from 0 indicating very strongly disagree to 6 indicating very strongly agree).

Statistical analysis

Region-specific descriptive statistics were calculated to describe the sample. Next, Pearson’s Chi-squared association test with Yates’ continuity correction was performed to examine plausible associations between psychosomatic complaints and other categorical study variables. Also, to account for the regional clustering or unobserved heterogeneity observed in the analytic sample, sequential mixed effect binary logit models with the inclusion of a random intercept were fitted to further examine the associations between psychosomatic complaints and cyberbullying victimisation as well as other considered covariates. Furthermore, a parallel mediator model was fitted to evaluate the specified hypothesis and understand the potential mechanism linking cyberbullying victimisation and psychosomatic complaints. More specifically, cyberbullying victimisation (X) was modelled to directly influence psychosomatic complaints (Y) and indirectly via PSMU (M). Since core variables were binary, paths could be estimated with a sequence of three logit equations: 60 , 61

where, \({i}_{1}\) , \({i}_{2}\) , and \({i}_{3}\) represent the intercept in the respective equations. The path coefficient, c, in Eq. ( 1 ) represents the total effect of predictor X on outcome Y . In Eq. ( 2 ), the path coefficient a denotes the effect of predictor X on the mediator M . Also, the c' parameter in Eq. ( 3 ) represents the direct effect of the predictor X on the response Y , adjusting for the mediator M . Lastly, the path coefficient b coefficient in Eq. ( 3 ) represents the indirect effect of the mediator M on the outcome Y , when adjusting for the predictor X . These logit models provide effect estimates on the log-odds scale, and thus can be transformed into odds ratios. Each model was adjusted for the potential confounding variables.

All statistical analyses were performed using R Software (v4.1.2; R Core Team 2021) with \(\alpha\)  =  0.05 as the significance level. More specifically, the package “mediation” in R 62 was used for the mediation analysis to estimate direct, indirect, and total effects. Inference is based on a non-parametric, 95% bias-corrected and accelerated (BCa) bootstrapped confidence interval 63 , 64 . Bootstrapping for indirect effects was set at 1000 samples, and once the 95% bootstrapped CI of the mediation effects did not include zero (0), it was deemed statistically significant. We also conducted further analysis by including an interaction between cyberbullying victimisation and PSMU to obtain insights analogous to the mediation model.

Ethics approval and consent to participate

The research was exclusively based on data sourced from the World Bank, which adheres to rigorous ethical standards in its data collection processes. Therefore, no separate ethical approval was sought or deemed necessary. Ethical approval was not required for this study since the data used for this study are secondary data. Necessary permissions and survey data were obtained from the World Bank. The World Bank data collection process upheld ethical standards and relevant guidelines in the research process including informed consent from all subjects and/or their legal guardian(s).

Preliminary analyses

The final analytic sample comprised complete information on 142,298 adolescents from 35 high-income countries (Table 1 ). The median age of the sample was 13.6 years. Most participants resided in Wales (6.26%) and the Czech Republic (6.16%). Notably, the prevalence of cyberbullying victimisation was 26.2%, and the majority (53%) were females. As observed in Table 2 , 84.6% of the participants self-reported high levels of psychosomatic complaints. Furthermore, among the participants who experienced PSMU, about 81.16% reported high levels of psychosomatic complaints. About 84.47% of the participants indicated receiving parental and peer support (see Table 2 ).

Main analyses

Results from the sequential binary mixed effects logit model are shown in Table 3 . In the first step, we included only cyberbullying victimisation in the model. We found that cyberbullying victims were 2.430 times more likely to report psychosomatic complaints than those who were not cyberbullied (OR = 2.430; 95%CI = 2.330, 2.530). The second step included sex, PSMU, parental and peer support, and family affluence as covariates. We found that cyber bullying victims were 2.390 times significantly more likely to report psychosomatic complaints than those who never experienced cyberbullying (AOR = 2.390; 95%CI = 2.29, 2.49). Additionally, the third model, which is an additional analysis involved the inclusion of an interaction between and cyberbullying victimisation and PSMU. The results showed that PSMU moderates the association between cyberbullying victimisation and psychosomatic complaints. Adolescents who were cyberbullied but did not report PSMU had reduced odds of psychosomatic complaints compared to those with PSMU (AOR = 1.220; 95%CI = 1.110–1.350). Furthermore, a caterpillar plot of empirical Bayes residuals of the models for the random intercept, region/country is obtained and shown in Fig.  1 . This represents individual effects for each country and offers additional insights into the extent of psychosomatic complaints heterogeneity across different countries. The plots visually demonstrates that regional variation for psychosomatic complaints does exist.

A caterpillar plot of empirical Bayes residuals of the models for the random intercept, region/country. This represents individual effects for each region/country. Region or country abbreviations in the figure are as follows: [AL] Albania, [AZ] Azerbaijan, [AT] Austria, [BE-VLG] Vlaamse Gewest (Belgium), [BE-WAL] Wallone, Région (Belgium), [CA] Canada, [CZ] Czech Republic, [DE] Germany, [EE] Estonia, [CA] Canada, [ES] Spain, [FR] France, [GB-ENG] England, [GB-SCT] Scotland, [GB-WLS] Wales, [GE] Georgia, [GR] Greece, [HR] Croatia, [HU] Hungary, [IE] Ireland, [IL] Israel, [IS] Iceland, [IT] Italy, [KZ] Kazakhstan, [LT] Lithuania, [LU] Luxembourg, [MD] Moldova, [MT] Malta, [NL] Netherlands, [PT] Portugal, [RO] Romania, [RS] Serbia, [RU] Russia, [SE] Sweden, [SI] Slovenia, [TR] Turkey, [LU] Luxembourg and [UA] Ukraine.

Figure  2 shows the adjusted parallel mediation results. The effect of cyberbullying victimisation on psychosomatic complaints was significantly mediated by PSMU. The paths from cyberbullying victimisation to PSMU (a: \(\beta\) =0.648, p < 0.001), PSMU to psychosomatic complaints (b: \(\beta\) =0.889, p < 0.001), and that of cyberbullying victimisation to 0.8069 (c′: \(\beta\) =0.051, p < 0.001) were also statistically significant.

A parallel mediation model of the influence of PSMU on the association between Cyberbullying Victimisation and Psychosomatic Complaints. a = path coefficient of the effect of exposure on the mediator. b = path coefficient of the effect of the mediator on the outcome. c’ = path coefficient of the direct effect of the exposure on outcome. CV, cyberbullying victimisation. PC, psychosomatic complaints.

Bootstrapping test of mediating effects

The total, direct, and indirect effects of the mediation model based on nonparametric bootstrap are presented in Table 4 . We observe that the estimated CI did not include zero (0) for any effects. This observation suggests a statistically significant indirect effect of cyberbullying victimisation on psychosomatic complaints via PSMU ( \(\beta\)  = 0.01162, 95%CI = 0.0110, 0.0120), yielding 12% of the total effect.

Key findings

This cross-cultural study examined the direct and indirect associations of cyberbullying victimisation with psychosomatic complaints via PSMU among adolescents. The results showed that cyberbullying victimisation independently influenced the experience of psychosomatic complaints. Specifically, adolescents who were victims of cyberbullying were more than two times more likely to report psychosomatic complaints. Crucially, our mediation analyses indicated that PSMU explain approximately 12% of the association between cyberbullying victimisation and psychosomatic complaints. In a further analysis, PSMU moderated the association between cyberbullying victimisation and psychosomatic complaints. This study is the first to examine the direct and indirect associations between cyberbullying victimisation and psychosomatic complaints through PSMU in adolescents across multiple high-income countries.

Interpretation of the findings

Our results confirmed the first hypothesis that there is a statistically significant direct association between cyberbullying victimisation and psychosomatic complaints. Thus, we found that cyberbullying independently directly affected the adolescents' experience of psychosomatic complaints. Previous studies have mainly focused on the direct effect of traditional face-to-face bullying on psychosomatic complaints 20 , 65 or compared the impact of traditional face-to-face bullying to cyberbullying concerning mental health 19 , 66 , 67 , 68 , 69 . A systematic review of traditional bullying and cyberbullying victimisation offers a comprehensive synthesis of the consequences of cyberbullying on adolescent health 19 . Another review suggested that cyberbullying threatened adolescents’ well-being and underscored many studies that have demonstrated effective relationships between adolescents’ involvement in cyberbullying and adverse health outcomes 70 . Other population-based cross-sectional studies have similarly shown that victims of cyberbullying experience significant psychological distress and feelings of isolation, which can further exacerbate their physical and mental health challenges 22 , 71 , 72 . The present study builds on the previously published literature by highlighting the effect of cyberbullying victimisation on adolescent psychosomatic complaints and the extent to which the association is mediated by PSMU.

Consistent with the second hypothesis, we found that PSMU mediated about 12% of the association between cyberbullying victimisation and psychosomatic complaints in this sample. While studies on the mediational role of PSMU in the relationship between cyberbullying victimisation and psychosomatic complaints are limited, evidence shows significant interplay among PSMU, cyberbullying victimisation, and psychosomatic complaints. For example, a study of over 58,000 young people in Italy found that PSMU was associated with increased levels of multiple somatic and psychological symptoms, such as anxiety and depression. 73 Another study of 1707 adolescents in Sweden found that cyberbullying victimisation was associated with increased depressive symptoms and the lowest level of subjective well-being 74 .

Other possible mediators of the cyberbullying victimisation-psychosomatic complaints association may include low self-esteem, negative body image, emotion regulation difficulties, social support, and personality traits such as neuroticism and impulsivity 20 , 67 , 72 , 75 , 76 . For example, Schneider et al. 75 have shown that emotional distress could increase psychosomatic symptoms such as headaches, stomach aches, and muscle tension. In addition, social isolation can lead to social withdrawal and a decreased sense of belonging 78 , 79 . Therefore, it is essential to explore these variables further and develop effective interventions and prevention strategies to address these interrelated factors and reduce their negative impact on adolescent health and well-being.

In a further analysis, the results show that PSMU does not only mediate but also moderate the association between cyberbullying victimisation and psychosomatic complaints among adolescents. Specifically, cyberbullied adolescents with no report of PSMU had reduced likelihoods of experiencing psychosomatic complaints compared to those with PSMU. This result is interesting and could be due to several factors. First, individuals with PSMU may already be experiencing heightened levels of psychological distress due to their excessive social media use, making them more vulnerable to the negative effects of cyberbullying 80 , 81 , 82 . For instance, excessive time spent on social media, particularly in activities such as comparing oneself to others or seeking validation through likes and comments, has been linked to increased psychological distress 83 , 84 . Conversely, the finding that cyberbullied adolescents without PSMU had reduced likelihoods of experiencing psychosomatic complaints compared to those with PSMU suggests a protective effect of lower social media use. Adolescents who are not excessively engaged with social media may have fewer opportunities for exposure to cyberbullying and may also have healthier coping strategies in place to deal with any instances of online victimisation 43 , 85 , 86 .

The results suggest that professionals in the fields of education, counselling, and healthcare should prioritise addressing the issue of cyberbullying victimisation when assessing the physical and psychological health of adolescents. Evidently, adolescents who experience cyberbullying require support. Thus, proactive measures are essential, and support could be provided by multiple professional communities that serve adolescents and young people in society, such as educational, behavioural health, and medical professionals. Sensitive inquiry regarding cyberbullying experiences is necessary when addressing adolescent health issues such as depression, substance use, suicidal ideation, and somatic concerns 19 . Our findings underscore the need for comprehensive, school-based programs focused on cyberbullying victimisation prevention and intervention.

Strengths and limitations

The study's main strength lies in the use of a large sample size representing multiple countries in high income countries. This large sample size improved the representativeness and veracity of our findings. The complex research approach helps advance our understanding of the interrelationships between cyberbullying victimisation, PSMU, and psychosomatic complaints among adolescents. However, the study has its limitations. First, the cross-sectional design does not allow directionality and causal inferences. Second, retrospective self-reporting for the critical study variables could lead to recall and social desirability biases. Third, the presence of residual and unobserved confounders, despite adjusting for some covariates, can be considered a limitation of this study. Further research is needed to confirm these findings and better understand how PSMU mediates the relationship between cyberbullying victimisation and psychosomatic complaints.

Conclusions

This study has provided essential insights into the interrelationships between cyberbullying victimisation, PSMU, and psychosomatic complaints among adolescents in high income countries. The findings suggest that cyberbullying is directly associated with psychosomatic complaints and that PSMU significantly and partially mediates this association. This study also highlights the importance of addressing cyberbullying victimisation and its negative impact on adolescent health and emphasises the need to address PSMU. Overall, the study underscores the importance of promoting healthy online behaviour and providing appropriate support for adolescents who experience cyberbullying victimisation. Further studies will benefit from longitudinal data to confirm our findings.

Data availability

The data that support the findings of this study are available from the World Bank, but restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. Data are, however, available from the corresponding author ([email protected]) upon reasonable request and with permission of the World Bank.

Lyyra, N., Välimaa, R. & Tynjälä, J. Loneliness and subjective health complaints among school-aged children. Scand. J. Public Health 46 (20), 87–93. https://doi.org/10.1177/1403494817743901 (2018).

Article   PubMed   Google Scholar  

Ottova, V. et al. The role of individual-and macro-level social determinants on young adolescents’ psychosomatic complaints. J. Early. 32 (1), 126–158. https://doi.org/10.1177/0272431611419510 (2012).

Article   Google Scholar  

Heinz, A., Catunda, C., van Duin, C. & Willems, H. Suicide prevention: Using the number of health complaints as an indirect alternative for screening suicidal adolescents. J. Affect. Disord. 260 , 61–66. https://doi.org/10.1016/j.jad.2019.08.025 (2020).

Högberg, B., Strandh, M. & Hagquist, C. Gender and secular trends in adolescent mental health over 24 years–the role of school-related stress. Soc. Cci Med. 250 , 112890. https://doi.org/10.1016/j.socscimed.2020.112890 (2020).

Hagquist, C., Due, P., Torsheim, T. & Välimaa, R. Cross-country comparisons of trends in adolescent psychosomatic symptoms—a Rasch analysis of HBSC data from four Nordic countries. Health Qual. Life Outcomes 17 (1), 1–13. https://doi.org/10.1186/s12955-019-1097-x (2019).

Shorey, S., Ng, E. D. & Wong, C. H. Global prevalence of depression and elevated depressive symptoms among adolescents: A systematic review and meta-analysis. Br. J. Clin. Psychol. 61 (2), 287–305. https://doi.org/10.1111/bjc.12333 (2022).

Potrebny, T. et al. Health complaints among adolescents in Norway: A twenty-year perspective on trends. PloS one 14 (1), e0210509. https://doi.org/10.1371/journal.pone.0210509 (2019).

Article   CAS   PubMed   PubMed Central   Google Scholar  

van Geelen, S. M. & Hagquist, C. Are the time trends in adolescent psychosomatic problems related to functional impairment in daily life? A 23-year study among 20,000 15–16 year olds in Sweden. J. Psychol. Res. 87 , 50–56. https://doi.org/10.1016/j.jpsychores.2016.06.003 (2016).

Swedish Association of Local Authorities and Regions and Ministry of Health and Social Affairs. Insatser inom området psykisk hälsa och suicidprevention. Överenskommelse mellan staten och Sveriges Kommuner och Regioner (SKR). Swedish Association of Local Authorities and Regions and Ministry of Health and Social Affairs. Stokholm, Sweden: 2021–2022. https://skr.se/skr/halsasjukvard/utvecklingavverksamhet/psykiskhalsa/overenskommelsepsykiskhalsa.234.html (2022).

Brooks, S. J., Feldman, I., Schiöth, H. B. & Titova, O. E. Important gender differences in psychosomatic and school-related complaints in relation to adolescent weight status. Sci. Rep. 11 (1), 14147. https://doi.org/10.1038/s41598-021-93761-0 (2021).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Whitehead, R. et al. Trends in adolescent overweight perception and its association with psychosomatic health 2002–2014: Evidence from 33 countries. J. Adol. Health 60 (2), 204–211. https://doi.org/10.1016/j.jadohealth.2016.09.029 (2017).

Nilsen, W., Karevold, E., Røysamb, E., Gustavson, K. & Mathiesen, K. S. Social skills and depressive symptoms across adolescence: Social support as a mediator in girls versus boys. J. Adol. 36 (1), 11–20. https://doi.org/10.1016/j.adolescence.2012.08.005 (2013).

Englander, E., Donnerstein, E., Kowalski, R., Lin, C. A. & Parti, K. Defining cyberbullying. Pediatric 140 (S2), 148–151. https://doi.org/10.1542/peds.2016-1758U (2017).

Chan, H. C. O. & Wong, D. S. Traditional school bullying and cyberbullying in Chinese societies: Prevalence and a review of the whole-school intervention approach. Aggress. Viol. Behav. 23 , 98–108. https://doi.org/10.1016/j.avb.2015.05.010 (2015).

Griffiths, M. D., Kuss, D. J. & Demetrovics, Z. Social networking addiction: An overview of preliminary findings. Behav Addict. 2014 , 119–141. https://doi.org/10.1016/B978-0-12-407724-9.00006-9 (2014).

Athanasiou, K. et al. Cross-national aspects of cyberbullying victimization among 14–17-year-old adolescents across seven European countries. BMC Public Health 18 , 1–15. https://doi.org/10.1186/s12889-018-5682-4 (2018).

Nagata, J. M. et al. Cyberbullying and Sleep Disturbance among Early Adolescents in the US. Acad. Pediatr. 23 (6), 1220–1225. https://doi.org/10.1016/j.acap.2022.12.007 (2022).

Fahy, A. E. et al. Longitudinal associations between cyberbullying involvement and adolescent mental health. J. Ado.l Health 59 (5), 502–509. https://doi.org/10.1016/j.jadohealth.2016.06.006 (2016).

Zych, I., Ortega-Ruiz, R. & Del Rey, R. Systematic review of theoretical studies on bullying and cyberbullying: Facts, knowledge, prevention, and intervention. Aggress. Viol. Behav. 23 , 1–21. https://doi.org/10.1016/j.avb.2015.10.001 (2015).

Kowalski, R. M. & Limber, S. P. Psychological, physical, and academic correlates of cyberbullying and traditional bullying. J. Adol. Health 53 (1), S13–S20. https://doi.org/10.1016/j.jadohealth.2012.09.018 (2013).

Van Geel, M., Vedder, P. & Tanilon, J. Relationship between peer victimization, cyberbullying, and suicide in children and adolescents: A meta-analysis. JAMA Pediatr. 168 (5), 435–442. https://doi.org/10.1001/jamapediatrics.2013.4143 (2014).

Article   CAS   PubMed   Google Scholar  

Albdour, M., Hong, J. S., Lewin, L. & Yarandi, H. The impact of cyberbullying on physical and psychological health of Arab American adolescents. J. Immig. Minor. Health 21 , 706–715. https://doi.org/10.1007/s10903-018-00850-w (2019).

Yoon, Y. et al. Association of cyberbullying involvement with subsequent substance use among adolescents. J. Adol. Health 65 (5), 613–620. https://doi.org/10.1016/j.jadohealth.2019.05.006 (2019).

Yuchang, J., Junyi, L., Junxiu, A., Jing, W. & Mingcheng, H. The differential victimization associated with depression and anxiety in cross-cultural perspective: A meta-analysis. Trauma Viol. Abuse 20 (4), 560–573. https://doi.org/10.1177/1524838017726426 (2019).

Gini, G. & Espelage, D. L. Peer victimization, cyberbullying, and suicide risk in children and adolescents. Jama 312 (5), 545–546. https://doi.org/10.1001/jama.2014.3212 (2014).

Tullett-Prado, D., Doley, J. R., Zarate, D., Gomez, R. & Stavropoulos, V. Conceptualising social media addiction: A longitudinal network analysis of social media addiction symptoms and their relationships with psychological distress in a community sample of adults. BMC Psychol. 23 (1), 1–27. https://doi.org/10.1186/s12888-023-04985-5 (2023).

Keles, B., McCrae, N. & Grealish, A. A systematic review: The influence of social media on depression, anxiety and psychological distress in adolescents. Int. J. Adol Youth 25 (1), 79–93. https://doi.org/10.1080/02673843.2019.1590851 (2020).

O’reilly, M. et al. Is social media bad for mental health and wellbeing? Exploring the perspectives of adolescents. Clin. Child Psychol. Psych. 23 (4), 601–613. https://doi.org/10.1177/1359104518775154 (2018).

Marino, C., Gini, G., Angelini, F., Vieno, A. & Spada, M. M. Social norms and e-motions in problematic social media use among adolescents. Addict. Behav. Rep. 11 , 100250. https://doi.org/10.1016/j.abrep.2020.100250 (2020).

Article   PubMed   PubMed Central   Google Scholar  

Sedgwick, R., Epstein, S., Dutta, R. & Ougrin, D. Social media, internet use and suicide attempts in adolescents. Curr. Opin. Psychol. 32 (6), 534. https://doi.org/10.1097/YCO.0000000000000547 (2019).

Marino, C., Hirst, C. M., Murray, C., Vieno, A. & Spada, M. M. Positive mental health as a predictor of problematic internet and Facebook use in adolescents and young adults. J. Happ. Stud. 19 , 2009–2022. https://doi.org/10.1007/s10902-017-9908-4 (2018).

Sarmiento, I. G. et al. How does social media use relate to adolescents’ internalizing symptoms? Conclusions from a systematic narrative review. Adol. Res. Rev. 5 , 381–404. https://doi.org/10.1007/s40894-018-0095-2 (2020).

Hanprathet, N., Manwong, M., Khumsri, J. M. S., Yingyeun, R. & Phanasathit, M. Facebook addiction and its relationship with mental health among Thai high school students. J. Med. Assoc. Thailand 98 , 81–90 (2015).

Google Scholar  

Cerutti, R. et al. Sleep disturbances partially mediate the association between problematic internet use and somatic symptomatology in adolescence. Curr. Psychol. 40 , 4581–4589. https://doi.org/10.1007/s12144-019-00414-7 (2021).

Van Den Eijnden, R., Koning, I., Doornwaard, S., Van Gurp, F. & Ter Bogt, T. The impact of heavy and disordered use of games and social media on adolescents’ psychological, social, and school functioning. J. Behav. Addit. 7 (3), 697–706. https://doi.org/10.1556/2006.7.2018.65 (2018).

Andreassen, C. S. & Pallesen, S. Social network site addiction-an overview. Curr. Pharma Des. 20 (25), 4053–4061. https://doi.org/10.2174/13816128113199990616 (2014).

Article   CAS   Google Scholar  

Andreassen, C. S. Online social network site addiction: A comprehensive review. Curr. Addit Rep. 2 (2), 175–184. https://doi.org/10.1007/s40429-015-0056-9 (2015).

Best, P., Manktelow, R. & Taylor, B. Online communication, social media and adolescent wellbeing: A systematic narrative review. Child. Youth Serv. Rev. 41 , 27–36. https://doi.org/10.1016/j.childyouth.2014.03.001 (2014).

Boer, M. et al. Adolescents’ intense and problematic social media use and their well-being in 29 countries. J. Adol. Health 66 (6), S89–S99. https://doi.org/10.1016/j.jadohealth.2020.02.014 (2020).

Inchley, J. et al . Adolescent alcohol-related behaviours: Trends and inequalities in the WHO European Region, 2002–2014: Observations from the Health Behaviour in School-aged Children (HBSC) WHO collaborative cross-national study. World Health Organization. Regional Office for Europe (2018). https://apps.who.int/iris/handle/10665/342239 .

Moor, I. et al. The 2017/18 Health Behaviour in School-aged Children (HBSC) study–mthodology of the World Health Organization’s child and adolescent health study. J. Health Monitor. 5 (3), 88. https://doi.org/10.25646/6904 (2020).

Nardone, P. et al. Dietary habits among Italian adolescents and their relation to socio-demographic characteristics. Ann. Istit. Super. Sanita 56 (4), 504–513. https://doi.org/10.4415/ANN_20_04_15 (2020).

Craig, W. et al. Social media use and cyber-bullying: A cross-national analysis of young people in 42 countries. J. Adol. Health 66 (6), S100–S108. https://doi.org/10.1016/j.jadohealth.2020.03.006 (2020).

Moor, I. et al. The 2017/18 Health Behaviour in School-aged Children (HBSC) study–methodology of the World Health Organization’s child and adolescent health study. J. Health Monitor. 5 (3), 88 (2020).

Inchley, J., Currie, D., Cosma, A. & Samdal, O. Health Behaviour in School-Aged Children (HBSC) Study Protocol: Background, Methodology and Mandatory Items for the 2017/18 Survey ; CAHRU: St Andrews, UK (2018).

Haugland, S. & Wold, B. Subjective health complaints in adolescence—reliability and validity of survey methods. J. Adol. 24 (5), 611–624. https://doi.org/10.1006/jado.2000.0393 (2001).

Khan, A., Khan, S. R. & Lee, E. Y. Association between lifestyle behaviours and mental health of adolescents: Evidence from the Canadian HBSC Surveys, 2002–2014. Int. J. Environ. Res. Public Health 19 (11), 6899. https://doi.org/10.3390/ijerph19116899 (2022).

Högberg, B., Strandh, M., Johansson, K. & Petersen, S. Trends in adolescent psychosomatic complaints: A quantile regression analysis of Swedish HBSC data 1985–2017. Scand. J. Public Health 2022 , 21094497. https://doi.org/10.1177/14034948221094497 (2022).

Bjereld, Y., Augustine, L., Turner, R., Löfstedt, P. & Ng, K. The association between self-reported psychosomatic complaints and bullying victimisation and disability among adolescents in Finland and Sweden. Scand. J. Public Health 2022 , 1089769. https://doi.org/10.1177/14034948221089769 (2022).

Heinz, A., van Duin, C., Kern, M. R., Catunda, C. & Willems, H. Trends from 2006–2018 in Health, Health Behaviour, Health Outcomes and Social Context of Adolescents in Luxembourg . University of Luxembourg (2020).  http://hdl.habndle.net/10993/42571 .

Gariepy, G., McKinnon, B., Sentenac, M. & Elgar, F. J. Validity and reliability of a brief symptom checklist to measure psychological health in school-aged children. Child Indic. Res. 9 , 471–484. https://doi.org/10.1007/s12187-015-9326-2 (2016).

Biswas, T. et al. Variation in the prevalence of different forms of bullying victimisation among adolescents and their associations with family, peer and school connectedness: A population-based study in 40 lower and middle income to high-income countries (LMIC-HICs). J. Child. Adol. Trauma 2022 , 1–11. https://doi.org/10.1007/s40653-022-00451-8 (2022).

Sasson, H., Tur-Sinai, A., Dvir, K. & Harel-Fisch, Y. The role of parents and peers in cyberbullying perpetration: Comparison among Arab and Jewish and youth in Israel. Child Indic. Res. 2022 , 1–21. https://doi.org/10.1007/s12187-022-09986-6 (2022).

Marengo, N. et al. Cyberbullying and problematic social media use: An insight into the positive role of social support in adolescents—data from the Health Behaviour in School-aged Children study in Italy. Public Health 199 , 46–50. https://doi.org/10.1016/j.puhe.2021.08.010 (2021).

Van den Eijnden, R. J. J. M., Lemmens, J. & Valkenburg, P. The social media disorder scale: Validity and psychometric properties. Comp. Hum. Behav. 61 (August), 478487. https://doi.org/10.1016/j.chb.2016.03.038 (2016).

Borraccino, A. et al. Problematic social media use and cyber aggression in Italian adolescents: The remarkable role of social support. Int. J. Environ. Res. Public Health 19 (15), 9763. https://doi.org/10.3390/ijerph19159763 (2022).

Hamre, R., Smith, O. R. F., Samdal, O. & Haug, E. Gaming behaviors and the association with sleep duration, social jetlag, and difficulties falling asleep among Norwegian adolescents. Int. J. Environ. Res. Public Health 19 (3), 1765. https://doi.org/10.3390/ijerph19031765 (2022).

Currie, C. et al. Researching health inequalities in adolescents: The development of the Health Behaviour in School-Aged Children (HBSC) family affluence scale. Soc. Sci Med. 66 (6), 1429–1436. https://doi.org/10.1016/j.socscimed.2007.11.024 (2008).

Zimet, G. D., Powell, S. S., Farley, G. K., Werkman, S. & Berkoff, K. A. Psychometric characteristics of the multidimensional scale of perceived social support. J. Person. Assess. 55 (3–4), 610–617. https://doi.org/10.1080/00223891.1990.9674095 (1990).

MacKinnon, D. P., Lockwood, C. M., Brown, C. H., Wang, W. & Hoffman, J. M. The intermediate endpoint effect in logistic and probit regression. Clin. Trial 4 (5), 499–513. https://doi.org/10.1177/1740774507083434 (2007).

Rijnhart, J. J., Valente, M. J., Smyth, H. L. & MacKinnon, D. P. Statistical mediation analysis for models with a binary mediator and a binary outcome: The differences between causal and traditional mediation analysis. Prevent. Sci. 2021 , 1–11. https://doi.org/10.1007/s11121-021-01308-6 (2021).

Tingley D, Yamamoto T, Hirose K, Keele L, Imai K, Yamamoto MT. Package ‘mediation’. Computer software manual. 2019 Sep 13:175-84.

DiCiccio, T. J. & Efron, B. Bootstrap confidence intervals. Stat. Sci. 11 (3), 189–228. https://doi.org/10.1214/ss/1032280214 (1996).

Article   MathSciNet   Google Scholar  

Preacher, K. J. & Hayes, A. F. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav. Res. Method 40 (3), 879–891. https://doi.org/10.3758/BRM.40.3.879 (2008).

Tomşa, R., Jenaro, C., Campbell, M. & Neacşu, D. Student’s experiences with traditional bullying and cyberbullying: Findings from a Romanian sample. Procedia-Soc. Behav. Sci. 78 , 586–590. https://doi.org/10.1016/j.sbspro.2013.04.356 (2013).

Baier, D., Hong, J. S., Kliem, S. & Bergmann, M. C. Consequences of bullying on adolescents’ mental health in Germany: Comparing face-to-face bullying and cyberbullying. J. Child Fam. Stud. 28 , 2347–2357. https://doi.org/10.1007/s10826-018-1181-6 (2019).

Beckman, L., Hagquist, C. & Hellström, L. Does the association with psychosomatic health problems differ between cyberbullying and traditional bullying?. Emot. Behav. Differ. 17 (3–4), 421–434. https://doi.org/10.1080/13632752.2012.704228 (2012).

Lazuras, L., Barkoukis, V. & Tsorbatzoudis, H. Face-to-face bullying and cyberbullying in adolescents: Trans-contextual effects and role overlap. Tech. Soc. 48 , 97–101. https://doi.org/10.1016/j.techsoc.2016.12.001 (2017).

Li, J., Sidibe, A. M., Shen, X. & Hesketh, T. Incidence, risk factors and psychosomatic symptoms for traditional bullying and cyberbullying in Chinese adolescents. Child. Youth Serv. Rev. 107 , 104511. https://doi.org/10.1016/j.childyouth.2019.104511 (2019).

Nixon, C. L. Current perspectives: The impact of cyberbullying on adolescent health. Adol. Health Med. Therapy 2014 , 143–158. https://doi.org/10.2147/AHMT.S36456 (2014).

Olenik-Shemesh, D., Heiman, T. & Eden, S. Cyberbullying victimisation in adolescence: Relationships with loneliness and depressive mood. Emot. Behav. Differ. 17 (3–4), 361–374. https://doi.org/10.1080/13632752.2012.704227 (2012).

Sourander, A. et al. Psychosocial risk factors associated with cyberbullying among adolescents: A population-based study. Arch. Gener. Psychiatry 67 (7), 720–728. https://doi.org/10.1001/archgenpsychiatry.2010.79 (2010).

Claudia, M. et al. Problematic social media use: Associations with health complaints among adolescents. Ann. Istit. Super. Sanità 56 (4), 514–521. https://doi.org/10.4415/ANN_20_04_16 (2020).

Hellfeldt, K., López-Romero, L. & Andershed, H. Cyberbullying and psychological well-being in young adolescence: The potential protective mediation effects of social support from family, friends, and teachers. Int.. J. Environ. Res. Public Health 17 (1), 45. https://doi.org/10.3390/ijerph17010045 (2020).

Gini, G. & Pozzoli, T. Bullied children and psychosomatic problems: A meta-analysis. Pediatrics 132 (4), 720–729. https://doi.org/10.1542/peds.2013-0614 (2013).

Landstedt, E. & Persson, S. Bullying, cyberbullying, and mental health in young people. Scand. J. Public Health 42 (4), 393–399. https://doi.org/10.1177/1403494814525 (2014).

Schneider, S. K., Odonnell, L., Stueve, A. & Coulter, R. W. Cyberbullying, school bullying, and psychological distress: A regional census of high school students. Am. J. Public Health 102 (1), 171–177. https://doi.org/10.2105/AJPH.2011.300308 (2012).

Brighi, A., Guarini, A., Melotti, G., Galli, S. & Genta, M. L. Predictors of victimisation across direct bullying, indirect bullying and cyberbullying. Emot. Behav. Differ. 17 (3–4), 375–388. https://doi.org/10.1080/13632752.2012.704684 (2012).

Cowie, H. Cyberbullying and its impact on young people’s emotional health and well-being. The Psychia 37 (5), 167–170. https://doi.org/10.1192/pb.bp.112.040840 (2013).

Berryman, C., Ferguson, C. J. & Negy, C. Social media use and mental health among young adults. Psych. Q. 89 , 307–314. https://doi.org/10.1007/s11126-017-9535-6 (2018).

Verduyn, P., Ybarra, O., Résibois, M., Jonides, J. & Kross, E. Do social network sites enhance or undermine subjective well-being? A critical review. Soc. Issue Policy Rev. 11 (1), 274–302. https://doi.org/10.1542/peds.2007-0693 (2017).

Vogel, E. A., Rose, J. P., Okdie, B. M., Eckles, K. & Franz, B. Who compares and despairs? The effect of social comparison orientation on social media use and its outcomes. Person. Individ. Differ. 86 , 249–256. https://doi.org/10.1016/j.paid.2015.06.026 (2015).

Keles, B., McCrae, N. & Grealish, A. A systematic review: The influence of social media on depression, anxiety and psychological distress in adolescents. Int. J. Adol. Youth 25 (1), 79–93. https://doi.org/10.1080/02673843.2019.1590851 (2020).

Boer, M. et al. Adolescents’ intense and problematic social media use and their well-being in 29 countries. J. Adol. Health 66 (6), 89–99. https://doi.org/10.1016/j.jadohealth.2020.02.014 (2020).

McHugh, B. C., Wisniewski, P., Rosson, M. B. & Carroll, J. M. When social media traumatizes teens: The roles of online risk exposure, coping, and post-traumatic stress. Int. Res. 28 (5), 1169–1188. https://doi.org/10.1108/IntR-02-2017-0077 (2018).

Trnka, R., Martínková, Z. & Tavel, P. An integrative review of coping related to problematic computer use in adolescence. Int. J. Public Health 61 , 317–327. https://doi.org/10.1007/s00038-015-0693-8 (2016).

Chen, L., Ho, S. S. & Lwin, M. O. A meta-analysis of factors predicting cyberbullying perpetration and victimization: From the social cognitive and media effects approach. New Media Soc. 19 (8), 1194–1213. https://doi.org/10.1177/1461444816634037 (2017).

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SYSTEMATIC REVIEW article

Cyberbullying among adolescents and children: a comprehensive review of the global situation, risk factors, and preventive measures.

• 1 School of Political Science and Public Administration, Wuhan University, Wuhan, China
• 2 School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
• 3 College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, United Kingdom

Background: Cyberbullying is well-recognized as a severe public health issue which affects both adolescents and children. Most extant studies have focused on national and regional effects of cyberbullying, with few examining the global perspective of cyberbullying. This systematic review comprehensively examines the global situation, risk factors, and preventive measures taken worldwide to fight cyberbullying among adolescents and children.

Methods: A systematic review of available literature was completed following PRISMA guidelines using the search themes “cyberbullying” and “adolescent or children”; the time frame was from January 1st, 2015 to December 31st, 2019. Eight academic databases pertaining to public health, and communication and psychology were consulted, namely: Web of Science, Science Direct, PubMed, Google Scholar, ProQuest, Communication & Mass Media Complete, CINAHL, and PsycArticles. Additional records identified through other sources included the references of reviews and two websites, Cyberbullying Research Center and United Nations Children's Fund. A total of 63 studies out of 2070 were included in our final review focusing on cyberbullying prevalence and risk factors.

Results: The prevalence rates of cyberbullying preparation ranged from 6.0 to 46.3%, while the rates of cyberbullying victimization ranged from 13.99 to 57.5%, based on 63 references. Verbal violence was the most common type of cyberbullying. Fourteen risk factors and three protective factors were revealed in this study. At the personal level, variables associated with cyberbullying including age, gender, online behavior, race, health condition, past experience of victimization, and impulsiveness were reviewed as risk factors. Likewise, at the situational level, parent-child relationship, interpersonal relationships, and geographical location were also reviewed in relation to cyberbullying. As for protective factors, empathy and emotional intelligence, parent-child relationship, and school climate were frequently mentioned.

Conclusion: The prevalence rate of cyberbullying has increased significantly in the observed 5-year period, and it is imperative that researchers from low and middle income countries focus sufficient attention on cyberbullying of children and adolescents. Despite a lack of scientific intervention research on cyberbullying, the review also identified several promising strategies for its prevention from the perspectives of youths, parents and schools. More research on cyberbullying is needed, especially on the issue of cross-national cyberbullying. International cooperation, multi-pronged and systematic approaches are highly encouraged to deal with cyberbullying.

Introduction

Childhood and adolescence are not only periods of growth, but also of emerging risk taking. Young people during these periods are particularly vulnerable and cannot fully understand the connection between behaviors and consequences ( 1 ). With peer pressures, the heat of passion, children and adolescents usually perform worse than adults when people are required to maintain self-discipline to achieve good results in unfamiliar situations. Impulsiveness, sensation seeking, thrill seeking, and other individual differences cause adolescents to risk rejecting standardized risk interventions ( 2 ).

About one-third of Internet users in the world are children and adolescents under the age of 18 ( 3 ). Digital technology provide a new form of interpersonal communication ( 4 ). However, surveys and news reports also show another picture in the Internet Age. The dark side of young people's internet usage is that they may bully or suffer from others' bullying in cyberspace. This behavior is also acknowledged as cyberbullying ( 5 ). Based on Olweus's definition, cyberbullying is usually regarded as bullying implemented through electronic media ( 6 , 7 ). Specifically, cyberbullying among children and adolescents can be summarized as the intentional and repeated harm from one or more peers that occurs in cyberspace caused by the use of computers, smartphones and other devices ( 4 , 8 – 12 ). In recent years, new forms of cyberbullying behaviors have emerged, such as cyberstalking and online dating abuse ( 13 – 15 ).

Although cyberbullying is still a relatively new field of research, cyberbullying among adolescents is considered to be a serious public health issue that is closely related to adolescents' behavior, mental health and development ( 16 , 17 ). The increasing rate of Internet adoption worldwide and the popularity of social media platforms among the young people have worsened this situation with most children and adolescents experiencing cyberbullying or online victimization during their lives. The confines of space and time are alleviated for bullies in virtual environments, creating new venues for cyberbullying with no geographical boundaries ( 6 ). Cyberbullying exerts negative effects on many aspects of young people's lives, including personal privacy invasion and psychological disorders. The influence of cyberbullying may be worse than traditional bullying as perpetrators can act anonymously and connect easily with children and adolescents at any time ( 18 ). In comparison with traditional victims, those bullied online show greater levels of depression, anxiety and loneliness ( 19 ). Self-esteem problems and school absenteeism have also proven to be related to cyberbullying ( 20 ).

Due to changes in use and behavioral patterns among the youth on social media, the manifestations and risk factors of cyberbullying have faced significant transformation. Further, as the boundaries of cyberbullying are not limited by geography, cyberbullying may not be a problem contained within a single country. In this sense, cyberbullying is a global problem and tackling it requires greater international collaboration. The adverse effects caused by cyberbullying, including reduced safety, lower educational attainment, poorer mental health and greater unhappiness, led UNICEF to state that “no child is absolutely safe in the digital world” ( 3 ).

Extant research has examined the prevalence and risk factors of cyberbullying to unravel the complexity of cyberbullying across different countries and their corresponding causes. However, due to variations in cyberbullying measurement and methodologies, no consistent conclusions have been drawn ( 21 ). Studies into inconsistencies in prevalence rates of cyberbullying, measured in the same country during the same time period, occur frequently. Selkie et al. systematically reviewed cyberbullying among American middle and high school students aged 10–19 years old in 2015, and revealed that the prevalence of cyberbullying victimization ranged from 3 to 72%, while perpetration ranged from 1 to 41% ( 22 ). Risk and protective factors have also been broadly studied, but confirmation is still needed of those factors which have more significant effects on cyberbullying among young people. Clarification of these issues would be useful to allow further research to recognize cyberbullying more accurately.

This review aims to extend prior contributions and provide a comprehensive review of cyberbullying of children and adolescents from a global perspective, with the focus being on prevalence, associated risk factors and protective factors across countries. It is necessary to provide a global panorama based on research syntheses to fill the gaps in knowledge on this topic.

Search Strategies

This study strictly employed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We consulted eight academic databases pertaining to public health, and communication and psychology, namely: Web of Science, Science Direct, PubMed, Google Scholar, ProQuest, Communication & Mass Media Complete, CINAHL, and PsycArticles. Additional records identified through other sources included the references of reviews and two websites, Cyberbullying Research Center and United Nations Children's Fund. With regard to the duration of our review, since most studies on cyberbullying arose around 2015 ( 9 , 21 ), this study highlights the complementary aspects of the available information about cyberbullying during the recent 5 year period from January 1st, 2015 to December 31st, 2019.

One researcher extracted keywords and two researchers proposed modifications. We used two sets of subject terms to review articles, “cyberbullying” and “child OR adolescent.” Some keywords that refer to cyberbullying behaviors and young people are also included, such as threat, harass, intimidate, abuse, insult, humiliate, condemn, isolate, embarrass, forgery, slander, flame, stalk, manhunt, as well as teen, youth, young people and student. The search formula is (cyberbullying OR cyber-bullying OR cyber-aggression OR ((cyber OR online OR electronic OR Internet) AND (bully * OR aggres * OR violence OR perpetrat * OR victim * OR threat * OR harass * OR intimidat * OR * OR insult * OR humiliate * OR condemn * OR isolate * OR embarrass * OR forgery OR slander * OR flame OR stalk * OR manhunt))) AND (adolescen * OR child OR children OR teen? OR teenager? OR youth? OR “young people” OR “elementary school student * ” OR “middle school student * ” OR “high school student * ”). The main search approach is title search. Search strategies varied according to the database consulted, and we did not limit the type of literature for inclusion. Journals, conference papers and dissertations are all available.

Specifically, the inclusion criteria for our study were as follows: (a). reported or evaluated the prevalence and possible risk factors associated with cyberbullying, (b). respondents were students under the age of 18 or in primary, junior or senior high schools, and (c). studies were written in English. Exclusion criteria were: (a). respondents came from specific groups, such as clinical samples, children with disabilities, sexual minorities, specific ethnic groups, specific faith groups or samples with cross-national background, (b). review studies, qualitative studies, conceptual studies, book reviews, news reports or abstracts of meetings, and (c). studies focused solely on preventive measures that were usually meta-analytic and qualitative in nature. Figure 1 presents the details of the employed screening process, showing that a total of 63 studies out of 2070 were included in our final review.

Figure 1 . PRISMA flow chart diagram showing the process of study selection for inclusion in the systematic review on children and adolescents cyberbullying.

Meta-analysis was not conducted as the limited research published within the 5 years revealed little research which reported odds ratio. On the other hand, due to the inconsistency of concepts, measuring instruments and recall periods, considerable variation could be found in research quality ( 23 ). Meta-analysis is not a preferred method.

Coding Scheme

For coding, we created a comprehensive code scheme to include the characteristics. For cyberbullying, we coded five types proposed by Willard ( 24 – 26 ), which included verbal violence, group violence, visual violence, impersonating and account forgery, and other behaviors. Among them, verbal violence is considered one of the most common types of cyberbullying and refers to the behavior of offensive responses, insults, mocking, threats, slander, and harassment. Group violence is associated with preventing others from joining certain groups or isolating others, forcing others to leave the group. Visual violence relates to the release and sharing of embarrassing photos and information without the owners' consent. Impersonating and account forgery refers to identity theft, stealing passwords, violating accounts and the creation of fake accounts to fraudulently present the behavior of others. Other behaviors include disclosure of privacy, sexual harassment, and cyberstalking. To comprehensively examine cyberbullying, we coded cyberbullying behaviors from both the perspectives of cyberbullying perpetrators and victims, if mentioned in the studies.

In relation to risk factors, we drew insights from the general aggression model, which contributes to the understanding of personal and situational factors in the cyberbullying of children and adolescents. We chose the general aggression model because (a) it contains more situational factors than other models (e.g., social ecological models) - such as school climate ( 9 ), and (b) we believe that the general aggression model is more suitable for helping researchers conduct a systematic review of cyberbullying risk and protective factors. This model provides a comprehensive framework that integrates domain specific theories of aggression, and has been widely applied in cyberbullying research ( 27 ). For instance, Kowalski and colleagues proposed a cyberbullying encounter through the general aggression model to understand the formation and development process of youth cyberbullying related to both victimization and perpetration ( 9 ). Victims and perpetrators enter the cyberbullying encounter with various individual characteristics, experiences, attitudes, desires, personalities, and motives that intersect to determine the course of the interaction. Correspondingly, the antecedents pertaining to cyberbullying are divided into two broad categories, personal factors and situational factors. Personal factors refer to individual characteristics, such as gender, age, motivation, personality, psychological states, socioeconomic status and technology use, values and perceptions, and other maladaptive behaviors. Situational factors focus on the provocation/support, parental involvement, school climate, and perceived anonymity. Consequently, our coders related to risk factors consisting of personal factors and situational factors from the perspectives of both cyberbullying perpetrators and victims.

We extracted information relating to individual papers and sample characteristics, including authors, year of publication, country, article type, sampling procedures, sample characteristics, measures of cyberbullying, and prevalence and risk factors from both cyberbullying perpetration and victimization perspectives. The key words extraction and coding work were performed twice by two trained research assistants in health informatics. The consistency test results are as follows: the Kappa value with “personal factors” was 0.932, and the Kappa value with “situational factors” was 0.807. The result shows that the coding consistency was high enough and acceptable. Disagreements were resolved through discussion with other authors.

Quality Assessment of Studies

The quality assessment of the studies is based on the recommended tool for assessing risk of bias, Cochrane Collaboration. This quality assessment tool focused on seven items: random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective reporting, and other sources of bias ( 28 ). We assessed each item as “low risk,” “high risk,” and “unclear” for included studies. A study is considered of “high quality” when it meets three or more “low risk” requirements. When one or more main flaw of a study may affect the research results, the study is considered as “low quality.” When a lack of information leads to a difficult judgement, the quality is considered to be “unclear.” Please refer to Appendix 1 for more details.

This comprehensive systematic review comprised a total of 63 studies. Appendices 2 , 3 show the descriptive information of the studies included. Among them, 58 (92%) studies measured two or more cyberbullying behavior types. The sample sizes of the youths range from several hundred to tens of thousands, with one thousand to five thousand being the most common. As for study distribution, the United States of America, Spain and China were most frequently mentioned. Table 1 presents the detail.

Table 1 . Descriptive information of studies included (2015–2019).

Prevalence of Global Cyberbullying

Prevalence across countries.

Among the 63 studies included, 22 studies reported on cyberbullying prevalence and 20 studies reported on prevalence from victimization and perpetration perspectives, respectively. Among the 20 studies, 11 national studies indicated that the prevalence of cyberbullying victimization and cyberbullying perpetration ranged from 14.6 to 52.2% and 6.3 to 32%, respectively. These studies were conducted in the United States of America ( N = 4) ( 29 – 32 ), South Korea ( N = 3) ( 33 – 35 ), Singapore ( N = 1) ( 36 ), Malaysia ( N = 1) ( 37 ), Israel ( N = 1) ( 38 ), and Canada ( N = 1) ( 39 ). Only one of these 11 national studies is from an upper middle income country, and the rest are from highincome countries identified by the World Bank ( 40 ). By combining regional and community-level studies, the prevalence of cyberbullying victimization and cyberbullying perpetration ranged from 13.99 to 57.5% and 6.0 to 46.3%, respectively. Spain reported the highest prevalence of cyberbullying victimization (57.5%) ( 41 ), followed by Malaysia (52.2%) ( 37 ), Israel (45%) ( 42 ), and China (44.5%) ( 43 ). The lowest reported victim rates were observed in Canada (13.99%) and South Korea (14.6%) ( 34 , 39 ). The reported prevalence of cyberbullying victimization in the United States of America ranged from 15.5 to 31.4% ( 29 , 44 ), while in Israel, rates ranged from 30 to 45% ( 26 , 42 ). In China, rates ranged from 6 to 46.3% with the country showing the highest prevalence of cyberbullying perpetration (46.30%) ( 15 , 43 , 45 , 46 ). Canadian and South Korean studies reported the lowest prevalence of cyberbullying perpetration at 7.99 and 6.3%, respectively ( 34 , 39 ).

A total of 10 studies were assessed as high quality studies. Among them, six studies came from high income countries, including Canada, Germany, Italy, Portugal, and South Korea ( 13 , 34 , 39 , 46 – 48 ). Three studies were from upper middle income countries, including Malaysia and China ( 37 , 43 ) and one from a lower middle income country, Nigeria ( 49 ). Figures 2 , 3 describe the prevalence of cyberbullying victimization and perpetration respectively among high quality studies.

Figure 2 . The prevalence of cyberbullying victimization of high quality studies.

Figure 3 . The prevalence of cyberbullying perpetration of high quality studies.

Prevalence of Various Cyberbullying Behaviors

For the prevalence of cyberbullying victimization and perpetration, the data were reported in 18 and 14 studies, respectively. Figure 4 shows the distribution characteristics of the estimated value of prevalence of different cyberbullying behaviors with box plots. The longer the box, the greater the degree of variation of the numerical data and vice versa. The rate of victimization and crime of verbal violence, as well as the rate of victimization of other behaviors, such as cyberstalking and digital dating abuse, has a large degree of variation. Among the four specified types of cyberbullying behaviors, verbal violence was regarded as the most commonly reported behaviors in both perpetration and victimization rates, with a wide range of prevalence, ranging from 5 to 18%. Fewer studies reported the prevalence data for visual violence and group violence. Studies also showed that the prevalence of impersonation and account forgery were within a comparatively small scale. Specific results were as follows.

Figure 4 . Cyberbullying prevalence across types (2015–2019).

Verbal Violence

A total of 13 studies reported verbal violence prevalence data ( 15 , 26 , 34 , 37 – 39 , 42 , 43 , 47 , 48 , 50 , 51 ). Ten studies reported the prevalence of verbal violence victimization ranging from 2.8 to 47.5%, while seven studies claimed perpetration prevalence ranging from 1.5 to 31.8%. Malaysia reported the highest prevalence of verbal violence victimization (47.5%) ( 37 ), followed by China (32%) ( 43 ). China reported that the prevalence of verbal violence victimization ranged from 5.1 to 32% ( 15 , 43 ). Israel reported that the prevalence of verbal violence victimization ranged from 3.4 to 18% ( 26 , 38 , 42 ). For perpetration rate, Malaysia reported the highest level at 31.8% ( 37 ), while a study for Spain reported the lowest, ranging from 3.2 to 6.4% ( 51 ).

Group Violence

The prevalence of group violence victimization was explored within 4 studies and ranged from 5 to 17.8% ( 26 , 34 , 42 , 43 ), while perpetration prevalence was reported in three studies, ranging from 10.1 to 19.07% ( 34 , 43 , 47 ). An Israeli study suggested that 9.8% of respondents had been excluded from the Internet, while 8.9% had been refused entry to a group or team ( 26 ). A study in South Korea argued that the perpetration prevalence of group violence was 10.1% ( 34 ), while a study in Italy reported that the rate of online group violence against others was 19.07% ( 47 ).

Visual Violence

The prevalence of visual violence victimization was explored within three studies and ranged from 2.6 to 12.1% ( 26 , 34 , 43 ), while the perpetration prevalence reported in four studies ranged from 1.7 to 6% ( 34 , 43 , 47 , 48 ). For victimization prevalence, a South Korean study found that 12.1% of respondents reported that their personal information was leaked online ( 34 ). An Israel study reported that the prevalence of outing the picture was 2.6% ( 26 ). For perpetration prevalence, a South Korean study found that 1.7% of respondents had reported that they had disclosed someone's personal information online ( 34 ). A German study reported that 6% of respondents had written a message (e.g., an email) to somebody using a fake identity ( 48 ).

Impersonating and Account Forgery

Four studies reported on the victimization prevalence of impersonating and account forgery, ranging from 1.1 to 10% ( 15 , 42 , 43 ), while five studies reported on perpetration prevalence, with the range being from 1.3 to 9.31% ( 15 , 43 , 47 , 48 , 51 ). In a Spanish study, 10% of respondents reported that their accounts had been infringed by others or that they could not access their account due to stolen passwords. In contrast, 4.5% of respondents reported that they had infringed other people's accounts or stolen passwords, with 2.5% stating that they had forged other people's accounts ( 51 ). An Israeli study reported that the prevalence of being impersonated was 7% ( 42 ), while in China, a study reported this to be 8.6% ( 43 ). Another study from China found that 1.1% of respondents had been impersonated to send dating-for-money messages ( 15 ).

Other Behaviors

The prevalence of disclosure of privacy, sexual harassment, and cyberstalking were also explored by scholars. Six studies reported the victimization prevalence of other cyberbullying behaviors ( 13 , 15 , 34 , 37 , 42 , 43 ), and four studies reported on perpetration prevalence ( 34 , 37 , 43 , 48 ). A study in China found that 1.2% of respondents reported that their privacy had been compromised without permission due to disputes ( 15 ). A study from China reported the prevalence of cyberstalking victimization was 11.9% ( 43 ), while a Portuguese study reported that this was 62% ( 13 ). In terms of perpetration prevalence, a Malaysian study reported 2.7% for sexual harassment ( 37 ).

Risk and Protective Factors of Cyberbullying

In terms of the risk factors associated with cyberbullying among children and adolescents, this comprehensive review highlighted both personal and situational factors. Personal factors referred to age, gender, online behavior, race, health conditions, past experiences of victimization, and impulsiveness, while situational factors consisted of parent-child relationship, interpersonal relationships, and geographical location. In addition, protective factors against cyberbullying included: empathy and emotional intelligence, parent-child relationship, and school climate. Table 2 shows the risk and protective factors for child and adolescent cyberbullying.

Table 2 . Risk and protective factors of cyberbullying among children and adolescents.

In terms of the risk factors associated with cyberbullying victimization at the personal level, many studies evidenced that females were more likely to be cyberbullied than males ( 13 , 26 , 29 , 38 , 43 , 52 , 54 , 55 , 58 ). Meanwhile, adolescents with mental health problems ( 61 ), such as depression ( 33 , 62 ), borderline personality disorder ( 63 ), eating disorders ( 41 ), sleep deprivation ( 56 ), and suicidal thoughts and suicide plans ( 64 ), were more likely to be associated with cyberbullying victimization. As for Internet usage, researchers agreed that youth victims were probably those that spent more time online than their counterparts ( 32 , 36 , 43 , 45 , 48 , 49 , 60 ). For situational risk factors, some studies have proven the relationship between cyberbullying victims and parental abuse, parental neglect, family dysfunction, inadequate monitoring, and parents' inconsistency in mediation, as well as communication issues ( 33 , 64 , 68 , 73 ). In terms of geographical location, some studies have reported that youths residing in city locations are more likely to be victims of cyberbullying than their peers from suburban areas ( 61 ).

Regarding the risk factors of cyberbullying perpetration at the personal level, it is generally believed that older teenagers, especially those aged over 15 years, are at greater risk of becoming cyberbullying perpetrators ( 55 , 67 ). When considering prior cyberbullying experiences, evidence showed that individuals who had experienced cyberbullying or face-to-face bullying tended to be aggressors in cyberbullying ( 35 , 42 , 49 , 51 , 55 ); in addition, the relationship between impulsiveness and cyberbullying perpetration was also explored by several pioneering scholars ( 55 , 72 , 80 ). The situational factors highlight the role of parents and teachers in cyberbullying experiences. For example, over-control and authoritarian parenting styles, as well as inharmonious teacher-student relationships ( 61 ) are perceived to lead to cyberbullying behaviors ( 74 , 75 ). In terms of differences in geographical locations, students residing in cities have a higher rate of online harassment than students living in more rural locations ( 49 ).

In terms of the protective factors in child and adolescent cyberbullying, scholars have focused on youths who have limited experiences of cyberbullying. At the personal level, high emotional intelligence, an ability for emotional self-control and empathy, such as cognitive empathy ability ( 44 , 55 ), were associated with lower rates of cyberbullying ( 57 ). At the situational level, a parent's role is seen as critical. For example, intimate parent-child relationships ( 46 ) and open active communication ( 19 ) were demonstrated to be related to lower experiences of cyberbullying and perpetration. Some scholars argued that parental supervision and monitoring of children's online activities can reduce their tendency to participate in some negative activities associated with cyberbullying ( 31 , 46 , 73 ). They further claimed that an authoritative parental style protects youths against cyberbullying ( 43 ). Conversely, another string of studies evidenced that parents' supervision of Internet usage was meaningless ( 45 ). In addition to conflicting roles of parental supervision, researchers have also looked into the role of schools, and posited that positive school climates contribute to less cyberbullying experiences ( 61 , 79 ).

Some risk factors may be protective factors under another condition. Some studies suggest that parental aggressive communication is related to severe cyberbullying victims, while open communication is a potential protective factor ( 19 ). Parental neglect, parental abuse, parental inconsistency in supervision of adolescents' online behavior, and family dysfunction are related to the direct or indirect harm of cyberbullying ( 33 , 68 ). Parental participation, a good parental-children relationship, communication and dialogue can enhance children's school adaptability and prevent cyberbullying behaviors ( 31 , 74 ). When parental monitoring reaches a balance between control and openness, it could become a protective factor against cyberbullying, and it could be a risk factor, if parental monitoring is too low or over-controlled ( 47 ).

Despite frequent discussion about the risk factors associated with cyberbullying among children and adolescents, some are still deemed controversial factors, such as age, race, gender, and the frequency of suffering on the internet. For cyberbullying victims, some studies claim that older teenagers are more vulnerable to cyberbullying ( 15 , 38 , 52 , 53 ), while other studies found conflicting results ( 26 , 33 ). As for student race, Alhajji et al. argued that non-white students were less likely to report cyberbullying ( 29 ), while Morin et al. observed no significant correlation between race and cyberbullying ( 52 ). For cyberbullying perpetration, Alvarez-Garcia found that gender differences may have indirect effects on cyberbullying perpetration ( 55 ), while others disagreed ( 42 , 61 , 68 – 70 ). Specifically, some studies revealed that males were more likely to become cyberbullying perpetrators ( 34 , 39 , 56 ), while Khurana et al. presented an opposite point of view, proposing that females were more likely to attack others ( 71 ). In terms of time spent on the Internet, some claimed that students who frequently surf the Internet had a higher chance of becoming perpetrators ( 49 ), while others stated that there was no clear and direct association between Internet usage and cyberbullying perpetration ( 55 ).

In addition to personal and situational factors, scholars have also explored other specific factors pertaining to cyberbullying risk and protection. For instance, mindfulness and depression were found to be significantly related to cyber perpetration ( 76 ), while eating disorder psychopathology in adolescents was associated with cyber victimization ( 41 ). For males who were familiar with their victims, such as family members, friends and acquaintances, they were more likely to be cyberstalking perpetrators than females or strangers, while pursuing desired closer relationships ( 13 ). In the school context, a lower social likability in class was identified as an indirect factor for cyberbullying ( 48 ).

This comprehensive review has established that the prevalence of global childhood and adolescent victimization from cyberbullying ranges from 13.99 to 57.5%, and that the perpetration prevalence ranges from 6.0 to 46.3%. Across the studies included in our research, verbal violence is observed as one of the most common acts of cyberbullying, including verbal offensive responses, insults, mocking, threats, slander, and harassment. The victimization prevalence of verbal violence is reported to be between 5 and 47.5%, and the perpetration prevalence is between 3.2 and 26.1%. Personal factors, such as gender, frequent use of social media platforms, depression, borderline personality disorder, eating disorders, sleep deprivation, and suicidal tendencies, were generally considered to be related to becoming a cyberbullying victim. Personal factors, such as high school students, past experiences, impulse, improperly controlled family education, poor teacher-student relationships, and the urban environment, were considered risk factors for cyberbullying perpetration. Situational factors, including parental abuse and neglect, improper monitoring, communication barriers between parents and children, as well as the urban environment, were also seen to potentially contribute to higher risks of both cyberbullying victimization and perpetration.

Increasing Prevalence of Global Cyberbullying With Changing Social Media Landscape and Measurement Alterations

This comprehensive review suggests that global cyberbullying rates, in terms of victimization and perpetration, were on the rise during the 5 year period, from 2015 to 2019. For example, in an earlier study conducted by Modecki et al. the average cyberbullying involvement rate was 15% ( 81 ). Similar observations were made by Hamm et al. who found that the median rates of youth having experienced bullying or who had bullied others online, was 23 and 15.2%, respectively ( 82 ). However, our systematic review summarized global children and adolescents cyberbullying in the last 5 years and revealed an average cyberbullying perpetration rate of 25.03%, ranging from 6.0 to 46.3%, while the average victimization was 33.08%, ranging from 13.99 to 57.5%. The underlying reason for increases may be attributed to the rapid changing landscape of social media and, in recent years, the drastic increase in Internet penetration rates. With the rise in Internet access, youths have greater opportunities to participate in online activities, provided by emerging social media platforms.

Although our review aims to provide a broader picture of cyberbullying, it is well-noted in extant research that difficulties exist in accurately estimating variations in prevalence in different countries ( 23 , 83 ). Many reasons exist to explain this. The first largely relates poor or unclear definition of the term cyberbullying; this hinders the determination of cyberbullying victimization and perpetration ( 84 ). Although traditional bullying behavior is well-defined, the definition cannot directly be applied to the virtual environment due to the complexity in changing online interactions. Without consensus on definitions, measurement and cyberbullying types may vary noticeably ( 83 , 85 ). Secondly, the estimation of prevalence of cyberbullying is heavily affected by research methods, such as recall period (lifetime, last year, last 6 months, last month, or last week etc.), demographic characteristics of the survey sample (age, gender, race, etc.), perspectives of cyberbullying experiences (victims, perpetrators, or both victim and perpetrator), and instruments (scales, study-specific questions) ( 23 , 84 , 86 ). The variety in research tools and instruments used to assess the prevalence of cyberbullying can cause confusion on this issue ( 84 ). Thirdly, variations in economic development, cultural backgrounds, human values, internet penetration rates, and frequency of using social media may lead to different conclusions across countries ( 87 ).

Acknowledging the Conflicting Role of the Identified Risk Factors With More Research Needed to Establish the Causality

Although this review has identified many personal and situational factors associated with cyberbullying, the majority of studies adopted a cross-sectional design and failed to reveal the causality ( 21 ). Nevertheless, knowledge on these correlational relationships provide valuable insights for understanding and preventing cyberbullying incidents. In terms of gender differences, females are believed to be at a higher risk of cyberbullying victimization compared to males. Two reasons may help to explain this. First, the preferred violence behaviors between two genders. females prefer indirect harassment, such as the spreading of rumors, while males tend toward direct bullying (e.g., assault) ( 29 ) and second, the cultural factors. From the traditional gender perspective, females tended to perceive a greater risk of communicating with others on the Internet, while males were more reluctant to express fear, vulnerability and insecurity when asked about their cyberbullying experiences ( 46 ). Females were more intolerant when experiencing cyberstalking and were more likely to report victimization experiences than males ( 13 ). Meanwhile, many researchers suggested that females are frequent users of emerging digital communication platforms, which increases their risk of unpleasant interpersonal contact and violence. From the perspective of cultural norms and masculinity, the reporting of cyberbullying is also widely acknowledged ( 37 ). For example, in addition, engaging in online activities is also regarded as a critical predictor for cyberbullying victimization. Enabled by the Internet, youths can easily find potential victims and start harassment at any time ( 49 ). Participating in online activities directly increases the chance of experiencing cyberbullying victimization and the possibility of becoming a victim ( 36 , 45 ). As for age, earlier involvement on social media and instant messaging tools may increase the chances of experiencing cyberbullying. For example, in Spain, these tools cannot be used without parental permission before the age of 14 ( 55 ). Besides, senior students were more likely to be more impulsive and less sympathetic. They may portray more aggressive and anti-social behaviors ( 55 , 72 ); hence senior students and students with higher impulsivity were usually more likely to become cyberbullying perpetrators.

Past experiences of victimization and family-related factors are another risk for cyberbullying crime. As for past experiences, one possible explanation is that young people who had experienced online or traditional school bullying may commit cyberbullying using e-mails, instant messages, and text messages for revenge, self-protection, or improving their social status ( 35 , 42 , 49 , 55 ). In becoming a cyberbullying perpetrator, the student may feel more powerful and superior, externalizing angry feelings and relieving the feelings of helplessness and sadness produced by past victimization experiences ( 51 ). As for family related factors, parenting styles are proven to be highly correlated to cyberbullying. In authoritative families, parents focus on rational behavioral control with clear rules and a high component of supervision and parental warmth, which have beneficial effects on children's lifestyles ( 43 ). Conversely, in indulgent families, children's behaviors are not heavily restricted and parents guide and encourage their children to adapt to society. The characteristics of this indulgent style, including parental support, positive communication, low imposition, and emotional expressiveness, possibly contribute to more parent-child trust and less misunderstanding ( 75 ). The protective role of warmth/affection and appropriate supervision, which are common features of authoritative or indulgent parenting styles, mitigate youth engagement in cyberbullying. On the contrary, authoritarian and neglectful styles, whether with excessive or insufficient control, are both proven to be risk factors for being a target of cyberbullying ( 33 , 76 ). In terms of geographical location, although several studies found that children residing in urban areas were more likely to be cyberbullying victims than those living in rural or suburban areas, we cannot draw a quick conclusion here, since whether this difference attributes to macro-level differences, such as community safety or socioeconomic status, or micro-level differences, such as teacher intervention in the classroom, courses provided, teacher-student ratio, is unclear across studies ( 61 ). An alternative explanation for this is the higher internet usage rate in urban areas ( 49 ).

Regarding health conditions, especially mental health, some scholars believe that young people with health problems are more likely to be identified as victims than people without health problems. They perceive health condition as a risk factor for cyberbullying ( 61 , 63 ). On the other hand, another group of scholars believe that cyberbullying has an important impact on the mental health of adolescents which can cause psychological distress consequences, such as post-traumatic stress mental disorder, depression, suicidal ideation, and drug abuse ( 70 , 87 ). It is highly possible that mental health could be risk factors, consequences of cyberbullying or both. Mental health cannot be used as standards, requirements, or decisive responses in cyberbullying research ( 13 ).

The Joint Effort Between Youth, Parents, Schools, and Communities to Form a Cyberbullying-Free Environment

This comprehensive review suggests that protecting children and adolescents from cyberbullying requires joint efforts between individuals, parents, schools, and communities, to form a cyberbullying-free environment. For individuals, young people are expected to improve their digital technology capabilities, especially in the use of social media platforms and instant messaging tools ( 55 ). To reduce the number of cyberbullying perpetrators, it is necessary to cultivate emotional self-regulation ability through appropriate emotional management training. Moreover, teachers, counselors, and parents are required to be armed with sufficient knowledge of emotional management and to develop emotional management capabilities and skills. In this way, they can be alert to the aggressive or angry emotions expressed by young people, and help them mediate any negative emotions ( 45 ), and avoid further anti-social behaviors ( 57 ).

For parents, styles of parenting involving a high level of parental involvement, care and support, are desirable in reducing the possibility of children's engagement in cyberbullying ( 74 , 75 ). If difficulties are encountered, open communication can contribute to enhancing the sense of security ( 73 ). In this vein, parents should be aware of the importance of caring, communicating and supervising their children, and participate actively in their children's lives ( 71 ). In order to keep a balance between control and openness ( 47 ), parents can engage in unbiased open communication with their children, and reach an agreement on the usage of computers and smart phones ( 34 , 35 , 55 ). Similarly, it is of vital importance to establish a positive communication channel with children ( 19 ).

For schools, a higher priority is needed to create a safe and positive campus environment, providing students with learning opportunities and ensuring that every student is treated equally. With a youth-friendly environment, students are able to focus more on their academic performance and develop a strong sense of belonging to the school ( 79 ). For countries recognizing collectivist cultural values, such as China and India, emphasizing peer attachment and a sense of collectivism can reduce the risk of cyberbullying perpetration and victimization ( 78 ). Besides, schools can cooperate with mental health agencies and neighboring communities to develop preventive programs, such as extracurricular activities and training ( 44 , 53 , 62 ). Specifically, school-based preventive measures against cyberbullying are expected to be sensitive to the characteristics of young people at different ages, and the intersection of race and school diversity ( 29 , 76 ). It is recommended that school policies that aim to embrace diversity and embody mutual respect among students are created ( 26 ). Considering the high prevalence of cyberbullying and a series of serious consequences, it is suggested that intervention against cyberbullying starts from an early stage, at about 10 years old ( 54 ). Schools can organize seminars to strengthen communication between teachers and students so that they can better understand the needs of students ( 61 ). In addition, schools should encourage cyberbullying victims to seek help and provide students with opportunities to report cyberbullying behaviors, such as creating online anonymous calls.

Conclusions and Limitations

The comprehensive study has reviewed related research on children and adolescents cyberbullying across different countries and regions, providing a positive understanding of the current situation of cyberbullying. The number of studies on cyberbullying has surged in the last 5 years, especially those related to risk factors and protective factors of cyberbullying. However, research on effective prevention is insufficient and evaluation of policy tools for cyberbullying intervention is a nascent research field. Our comprehensive review concludes with possible strategies for cyberbullying prevention, including personal emotion management, digital ability training, policy applicability, and interpersonal skills. We highlight the important role of parental control in cyberbullying prevention. As for the role of parental control, it depends on whether children believe their parents are capable of adequately supporting them, rather than simply interfering in their lives, restricting their online behavior, and controlling or removing their devices ( 50 ). In general, cyberbullying is on the rise, with the effectiveness of interventions to meet this problem still requiring further development and exploration ( 83 ).

Considering the overlaps between cyberbullying and traditional offline bullying, future research can explore the unique risk and protective factors that are distinguishable from traditional bullying ( 86 ). To further reveal the variations, researchers can compare the outcomes of interventions conducted in cyberbullying and traditional bullying preventions simultaneously, and the same interventions only targeting cyberbullying ( 88 ). In addition, cyberbullying also reflects a series of other social issues, such as personal privacy and security, public opinion monitoring, multinational perpetration and group crimes. To address this problem, efforts from multiple disciplines and novel analytical methods in the digital era are required. As the Internet provides enormous opportunities to connect young people from all over the world, cyberbullying perpetrators may come from transnational networks. Hence, cyberbullying of children and adolescents, involving multiple countries, is worth further attention.

Our study has several limitations. First, national representative studies are scarce, while few studies from middle and low income countries were included in our research due to language restrictions. Many of the studies included were conducted in schools, communities, provinces, and cities in high income countries. Meanwhile, our review only focused on victimization and perpetration. Future studies should consider more perspectives, such as bystanders and those with the dual identity of victim/perpetrator, to comprehensively analyze the risk and protective factors of cyberbullying.

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author/s.

Author Contributions

SH, CZ, RE, and WZ conceived the study and developed the design. WZ analyzed the result and supervised the study. CZ and SH wrote the first draft. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2021.634909/full#supplementary-material

1. Ang RP. Adolescent cyberbullying: a review of characteristics, prevention and intervention strategies. Aggress Violent Behav. (2015) 25:35–42. doi: 10.1016/j.avb.2015.07.011

CrossRef Full Text | Google Scholar

2. Reyna VF, Farley F. Risk and rationality in adolescent decision making: implications for theory, practice, and public policy. Psychol Sci Public Interest. (2006) 7:1–44. doi: 10.1111/j.1529-1006.2006.00026.x

PubMed Abstract | CrossRef Full Text | Google Scholar

3. UNICEF ed. Children in a Digital World . New York, NY: UNICEF (2017).

Google Scholar

4. Thomas HJ, Connor JP, Scott JG. Integrating traditional bullying and cyberbullying: challenges of definition and measurement in adolescents - a review. Educ Psychol Rev. (2015) 27:135–52. doi: 10.1007/s10648-014-9261-7

5. Baldry AC, Farrington DP, Sorrentino A. “Am I at risk of cyberbullying”? A narrative review and conceptual framework for research on risk of cyberbullying and cybervictimization: the risk and needs assessment approach. Aggress Violent Behav. (2015) 23:36–51. doi: 10.1016/j.avb.2015.05.014

6. Olweus D. Bullying at School: What We Know and What We Can Do . Oxford; Cambridge, MA: Blackwell (1993).

PubMed Abstract | Google Scholar

7. Dooley JJ, Pyzalski J, Cross D. Cyberbullying versus face-to-face bullying: a theoretical and conceptual review. J Psychol. (2009) 217:182–8. doi: 10.1027/0044-3409.217.4.182

8. Smith PK, Mahdavi J, Carvalho M, Fisher S, Russell S, Tippett N. Cyberbullying: its nature and impact in secondary school pupils. J Child Psychol Psychiatry. (2008) 49:376–85. doi: 10.1111/j.1469-7610.2007.01846.x

9. Kowalski RM, Giumetti GW, Schroeder AN, Lattanner MR. Bullying in the digital age: a critical review and meta-analysis of cyberbullying research among youth. Psychol Bull. (2014) 140:1073–137. doi: 10.1037/a0035618

10. León Vicente I. Cybervictimization by cyberbullying: children at risk and children as risk (dissertation). University of the Basque Country, Leioa, Spain (2016).

11. Hinduja S, Patchin JW. Identification, Prevention, and Response. (2020).

12. Jadambaa A, Thomas HJ, Scott JG, Graves N, Brain D, Pacella R. Prevalence of traditional bullying and cyberbullying among children and adolescents in Australia: a systematic review and meta-analysis. Aust N Z J Psychiatry. (2019) 53:878–88. doi: 10.1177/0004867419846393

13. Pereira F, Matos M. Cyber-stalking victimization: what predicts fear among Portuguese adolescents? Eur J Crim Policy Res. (2016) 22:253–70. doi: 10.1007/s10610-015-9285-7

14. Reed LA, Ward LM, Tolman RM, Lippman JR, Seabrook RC. The association between stereotypical gender and dating beliefs and digital dating abuse perpetration in adolescent dating relationships. J Interpers Violence . (2018). doi: 10.1177/0886260518801933

15. Huang CL, Yang SC, Hsieh LS. The cyberbullying behavior of Taiwanese adolescents in an online gaming environment. Children Youth Serv Rev. (2019) 106:104461. doi: 10.1016/j.childyouth.2019.104461

16. Raskauskas J, Huynh A. The process of coping with cyberbullying: a systematic review. Aggress Violent Behav. (2015) 23:118–25. doi: 10.1016/j.avb.2015.05.019

17. Bradshaw J, Crous G, Rees G, Turner N. Comparing children's experiences of schools-based bullying across countries. Children Youth Serv Rev. (2017) 80:171–80. doi: 10.1016/j.childyouth.2017.06.060

18. Hutson E, Kelly S, Militello LK. Systematic review of cyberbullying interventions for youth and parents with implications for evidence-based practice. Worldviews Evid Based Nurs. (2018) 15:72–9. doi: 10.1111/wvn.12257

19. Larranaga E, Yubero S, Ovejero A, Navarro R. Loneliness, parent-child communication and cyberbullying victimization among Spanish youths. Comp Hum Behav. (2016) 65:1–8. doi: 10.1016/j.chb.2016.08.015

20. van Geel M, Vedder P, Tanilon J. Relationship between peer victimization, cyberbullying, and suicide in children and adolescents: a meta-analysis. JAMA Pediatr. (2014) 168:435–42. doi: 10.1001/jamapediatrics.2013.4143

21. Zych I, Ortega-Ruiz R, Del Rey R. Systematic review of theoretical studies on bullying and cyberbullying: facts, knowledge, prevention, and intervention. Aggress Violent Behav. (2015) 23:1–21. doi: 10.1016/j.avb.2015.10.001

22. Selkie EM, Kota R, Chan Y-F, Moreno M. Cyberbullying, depression, and problem alcohol use in female college students: a multisite study. Cyberpsychol Behav Soc Netw. (2015) 18:79–86. doi: 10.1089/cyber.2014.0371

23. Brochado S, Soares S, Fraga S. A scoping review on studies of cyberbullying prevalence among adolescents. Trauma Violence Abus. (2017) 18:523–31. doi: 10.1177/1524838016641668

24. Nocentini A, Calmaestra J, Schultze-Krumbholz A, Scheithauer H, Ortega R, Menesini E. Cyberbullying: labels, behaviours and definition in three European Countries. Aust J Guid Couns. (2010) 20:129–42. doi: 10.1375/ajgc.20.2.129

25. Willard NE. Cyberbullying and Cyberthreats: Responding to the Challenge of Online Social Aggression, Threats, and Distress . Champaign: Research Press (2007).

26. Aizenkot D, Kashy-Rosenbaum G. Cyberbullying victimization in whatsapp classmate groups among Israeli Elementary, Middle, and High School Students. J Interpers Violence . (2019). doi: 10.1177/0886260519842860

27. Anderson CA, Bushman BJ. Human aggression. Ann Rev Psychol. (2002) 53:27–51. doi: 10.1146/annurev.psych.53.100901.135231

28. Higgins JP, Green S. Cochrane Handbook for Systematic Reviews of Interventions . (2011). Available online at: http://handbook-5-1.cochrane.org/ (accessed January 17, 2021).

29. Alhajji M, Bass S, Dai T. Cyberbullying, mental health, and violence in adolescents and associations with sex and race: data from the 2015 youth risk behavior survey. Global Pediatr Health . (2019) 6:2333794X19868887. doi: 10.1177/2333794X19868887

30. Grinshteyn E, Yang YT. The association between electronic bullying and school absenteeism among high school students in the United States. J School Health. (2017) 87:142–9. doi: 10.1111/josh.12476

31. Mesch GS. Parent-child connections on social networking sites and cyberbullying. Youth Soc. (2018) 50:1145–62. doi: 10.1177/0044118X16659685

32. Sam J, Wisniewski P, Xu H, Rosson MB, Carroll JM. How are social capital and parental mediation associated with cyberbullying and cybervictimization among youth in the United States? In: Stephanidis C, editor. HCI International 2017 – Posters' Extended Abstracts Communications in Computer Information Science Cham: Springer International Publishing. p. 638–644.

33. Hong JS, Kim DH, Thornberg R, Kang JH, Morgan JT. Correlates of direct and indirect forms of cyberbullying victimization involving South Korean adolescents: an ecological perspective. Comput Hum Behav. (2018) 87:327–36. doi: 10.1016/j.chb.2018.06.010

34. Lee C, Shin N. Prevalence of cyberbullying and predictors of cyberbullying perpetration among Korean adolescents. Comp Hum Behav. (2017) 68:352–8. doi: 10.1016/j.chb.2016.11.047

35. You S, Lim SA. Longitudinal predictors of cyberbullying perpetration: evidence from Korean middle school students. Person Ind Differ. (2016) 89:172–6. doi: 10.1016/j.paid.2015.10.019

36. Holt TJ, Fitzgerald S, Bossler AM, Chee G, Ng E. Assessing the risk factors of cyber and mobile phone bullying victimization in a nationally representative sample of Singapore Youth. Int J Offend Ther Comp Criminol. (2016) 60:598–615. doi: 10.1177/0306624X14554852

37. Marret MJ, Choo WY. Factors associated with online victimisation among Malaysian adolescents who use social networking sites: a cross-sectional study. BMJ Open. (2017) 7:e014959. doi: 10.1136/bmjopen-2016-014959

38. Tesler R, Nissanholtz-Gannot R, Zigdon A, Harel-Fisch Y. The association of cyber-bullying and adolescents in religious and secular schools in Israel. J Relig Health. (2019) 58:2095–109. doi: 10.1007/s10943-019-00938-z

39. Beran T, Mishna F, McInroy LB, Shariff S. Children's experiences of cyberbullying: a Canadian National Study. Child School. (2015) 37:207–14. doi: 10.1093/cs/cdv024

40. World Bank Country and Lending Groups – World Bank Data Help Desk. Available online at: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups [accessed January 25, 2021).

41. Marco JH, Tormo-Irun P. Cyber victimization is associated with eating disorder psychopathology in adolescents. Front Psychol. (2018) 9:987. doi: 10.3389/fpsyg.2018.00987

42. Olenik-Shemesh D, Heiman T. Cyberbullying victimization in adolescents as related to body esteem, social support, and social self-efficacy. J Genet Psychol. (2017) 178:28–43. doi: 10.1080/00221325.2016.1195331

43. Rao J, Wang H, Pang M, Yang J, Zhang J, Ye Y, et al. Cyberbullying perpetration and victimisation among junior and senior high school students in Guangzhou, China. Inj Prev. (2019) 25:13–9. doi: 10.1136/injuryprev-2016-042210

44. Lee C. Weak Commitment to School, Deviant Peers, and Cyberbullying Victimization-Strain in Adolescent Cyberbullying . (2017). Available online at: https://search.proquest.com/pqdtglobal/docview/1937915933/abstract/9D85437600564444PQ/30 (accessed June 17, 2020).

45. Lin MT. Risk factors associated with cyberbullying victimization and perpetration among Taiwan children (dissertation). The University of Texas, Austin, TX, United States (2019).

46. Pieschl S, Porsch T. The complex relationship between cyberbullying and trust. Int J Dev Sci. (2017) 11:9–17. doi: 10.3233/DEV-160208

47. Brighi A, Menin D, Skrzypiec G, Guarini A. Young, bullying, and connected. Common pathways to cyberbullying and problematic internet use in adolescence. Front Psychol. (2019) 10:1467. doi: 10.3389/fpsyg.2019.01467

48. Festl R. Perpetrators on the internet: analyzing individual and structural explanation factors of cyberbullying in school context. Comp Hum Behav. (2016) 59:237–48. doi: 10.1016/j.chb.2016.02.017

49. Olumide AO, Adams P, Amodu OK. Prevalence and correlates of the perpetration of cyberbullying among in-school adolescents in Oyo State, Nigeria. Int J Adolesc Med Health. (2016) 28:183–91. doi: 10.1515/ijamh-2015-0009

50. Baldry AC, Sorrentino A, Farrington DP. Cyberbullying and cybervictimization versus parental supervision, monitoring and control of adolescents' online activities. Child Youth Serv Rev. (2019) 96:302–7. doi: 10.1016/j.childyouth.2018.11.058

51. Garaigordobil M. Cyberbullying in adolescents and youth in the Basque Country: prevalence of cybervictims, cyberaggressors, and cyberobservers. J Youth Stud. (2015) 18:569–82. doi: 10.1080/13676261.2014.992324

52. Morin HK, Bradshaw CP, Kush JM. Adjustment outcomes of victims of cyberbullying: the role of personal and contextual factors. J School Psychol. (2018) 70:74–88. doi: 10.1016/j.jsp.2018.07.002

53. Baraldsnes D. The prevalence of cyberbullying and the views of 5-12 grade pupils and teachers on cyberbullying prevention in Lithuanian Schools. Uinv J Educ Res. (2015) 3:949–59. doi: 10.13189/ujer.2015.031201

54. Razjouyan K, Mobarake AH, Sadr SS, Ardestani SMS, Yaseri M. The relationship between emotional intelligence and the different roles in cyberbullying among high school students in Tehran. Iran J Psychiatry Behav Sci. (2018) 12:UNSP e11560. doi: 10.5812/ijpbs.11560

55. Alvarez-Garcia D, Carlos Nunez J, Garcia T, Barreiro-Collazo A. Individual, family, and community predictors of cyber-aggression among adolescents. Eur J Psychol Appl Legal Context. (2018) 10:79–88. doi: 10.5093/ejpalc2018a8

56. Horzum MB, Ayas T, Randler C, Dusunceli B. The effects of empathy and circadian preference on cyberbullying of adolescents in Turkey. Biol Rhythm Res . (2019). doi: 10.1080/09291016.2019.1603839

57. Carmen Martinez-Monteagudo M, Delgado B, Manuel Garcia-Fernandez J, Rubio E. Cyberbullying, aggressiveness, and emotional intelligence in adolescence. Int J Environ Res Public Health. (2019) 16:5079. doi: 10.3390/ijerph16245079

58. Sasson H, Mesch G. The role of parental mediation and peer norms on the likelihood of cyberbullying. J Genet Psychol. (2017) 178:15–27. doi: 10.1080/00221325.2016.1195330

59. Wang X, Lei L, Liu D, Hu H. Moderating effects of moral reasoning and gender on the relation between moral disengagement and cyberbullying in adolescents. Person Ind Differ. (2016) 98:244–9. doi: 10.1016/j.paid.2016.04.056

60. Simsek N, Sahin D, Evli M. Internet addiction, cyberbullying, and victimization relationship in adolescents a sample from Turkey. J Addict Nurs. (2019) 30:201–10. doi: 10.1097/JAN.0000000000000296

61. McQuillan BE. Ecological Factors Associated with Middle School Students' Experiences of Cyberbullying . (2016). Available online at: https://search.proquest.com/pqdtglobal/docview/1794167537/abstract/9D85437600564444PQ/4 (accessed June 17, 2020).

62. Rose CA, Tynes BM. Longitudinal associations between cybervictimization and mental health among U.S. adolescents. J Adolesc Health. (2015) 57:305–12. doi: 10.1016/j.jadohealth.2015.05.002

63. Stockdale LA, Coyne SM, Nelson DA, Erickson DH. Borderline personality disorder features, jealousy, and cyberbullying in adolescence. Pers Individ Differ. (2015) 83:148–53. doi: 10.1016/j.paid.2015.04.003

64. Chen Q, Lo Camilla KM, Yuhong Z, Anne C, Ling CK, Patrick I. Family poly-victimization and cyberbullying among adolescents in a Chinese school sample. Child Abuse Negl. (2018) 77:180–7. doi: 10.1016/j.chiabu.2018.01.015

65. Landoll RR, La Greca AM, Lai BS, Chan SF, Herge WM. Cyber victimization by peers: prospective associations with adolescent social anxiety and depressive symptoms. J Adolesc. (2015) 42:77–86. doi: 10.1016/j.adolescence.2015.04.002

66. Iranzo B, Buelga S, Cava M-J, Ortega-Baron J. Cyberbullying, psychosocial adjustment, and suicidal ideation in adolescence. Psychosoc Interv. (2019) 28:75–81. doi: 10.5093/pi2019a5

67. Buelga S, Cava MJ, Musitu G, Torralba E. Cyberbullying aggressors among Spanish secondary education students: an exploratory study. Interact Tech Smart Ed. (2015) 12:100–15. doi: 10.1108/ITSE-08-2014-0025

68. Katz I, Lemish D, Cohen R, Arden A. When parents are inconsistent: parenting style and adolescents' involvement in cyberbullying. J Adolesc. (2019) 74:1–2. doi: 10.1016/j.adolescence.2019.04.006

69. Cénat JM, Blais M, Lavoie F, Caron P-O, Hébert M. Cyberbullying victimization and substance use among Quebec high schools students: the mediating role of psychological distress. Comp Hum Behav. (2018) 89:207–12. doi: 10.1016/j.chb.2018.08.014

70. Hoareau N, Bages C, Allaire M, Guerrien A. The role of psychopathic traits and moral disengagement in cyberbullying among adolescents. Crim Behav Ment Health. (2019) 29:321–31. doi: 10.1002/cbm.2135

71. Khurana A, Bleakley A, Jordan A, Romer D. The protective effects of parental monitoring and internet restriction on adolescents' risk of online harassment. J Youth Adolesc. (2015) 44:1039–47. doi: 10.1007/s10964-014-0242-4

72. Martínez I, Murgui S, Garcia OF, Garcia F. Parenting in the digital era: protective and risk parenting styles for traditional bullying and cyberbullying victimization. Comp Hum Behav. (2019) 90:84–92. doi: 10.1016/j.chb.2018.08.036

73. Yusuf S, Salleh H, Bahaman A, Shamsul M, Ramli N, Ramli AN, et al. Parental attachment and cyberbullying experiences among Malaysian children. Pertanika J Scholarly Res Rev . (2018) 4:67–80.

74. Martinez-Ferrer B, Leon-Moreno C, Musitu-Ferrer D, Romero-Abrio A, Callejas-Jeronimo JE, Musitu-Ochoa G. Parental socialization, school adjustment and cyber-aggression among adolescents. Int J Environ Res Public Health. (2019) 16:4005. doi: 10.3390/ijerph16204005

75. Moreno–Ruiz D, Martínez–Ferrer B, García–Bacete F. Parenting styles, cyberaggression, and cybervictimization among adolescents. Comp Hum Behav. (2019) 93:252–9. doi: 10.1016/j.chb.2018.12.031

76. Ho SS, Chen L, Ng APY. Comparing cyberbullying perpetration on social media between primary and secondary school students. Comp Educ. (2017) 109:74–84. doi: 10.1016/j.compedu.2017.02.004

77. Gómez-Ortiz O, Romera EM, Ortega-Ruiz R, Del Rey R. Parenting practices as risk or preventive factors for adolescent involvement in cyberbullying: contribution of children and parent gender. Int J Environ Res Public Health. (2018) 15:2664. doi: 10.3390/ijerph15122664

78. Wright MF, Kamble SV, Soudi SP. Indian adolescents' cyber aggression involvement and cultural values: the moderation of peer attachment. Sch Psychol Int. (2015) 36:410–27. doi: 10.1177/0143034315584696

79. Holfeld B, Leadbeater BJ. Concurrent and longitudinal associations between early adolescents' experiences of school climate and cyber victimization. Comput Hum Behav. (2017) 76:321–8. doi: 10.1016/j.chb.2017.07.037

80. Álvarez-García D, Núñez JC, González-Castro P, Rodríguez C, Cerezo R. The effect of parental control on cyber-victimization in adolescence: the mediating role of impulsivity and high-risk behaviors. Front Psychol. (2019) 10:1159. doi: 10.3389/fpsyg.2019.01159

81. Modecki KL, Minchin J, Harbaugh AG, Guerra NG, Runions KC. Bullying prevalence across contexts: a meta-analysis measuring cyber and traditional bullying. J Adolesc Health. (2014) 55:602–11. doi: 10.1016/j.jadohealth.2014.06.007

82. Hamm MP, Newton AS, Chisholm A, Shulhan J, Milne A, Sundar P, et al. Prevalence and effect of cyberbullying on children and young people: a scoping review of social media studies. JAMA Pediatr. (2015) 169:770. doi: 10.1001/jamapediatrics.2015.0944

83. Gaffney H, Farrington DP, Espelage DL, Ttofi MM. Are cyberbullying intervention and prevention programs effective? A systematic and meta-analytical review. Aggress Violent Behav. (2019) 45:134–53. doi: 10.1016/j.avb.2018.07.002

84. Selkie EM, Fales JL, Moreno MA. Cyberbullying prevalence among US middle and high school-aged adolescents: a systematic review and quality assessment. J Adolesc Health. (2016) 58:125–33. doi: 10.1016/j.jadohealth.2015.09.026

85. Ybarra ML, Boyd D, Korchmaros JD, Oppenheim J. Defining and measuring cyberbullying within the larger context of bullying victimization. J Adolesc Health. (2012) 51:53–8. doi: 10.1016/j.jadohealth.2011.12.031

86. Kowalski RM, Limber SP, McCord A. A developmental approach to cyberbullying: prevalence and protective factors. Aggress Violent Behav. (2019) 45:20–32. doi: 10.1016/j.avb.2018.02.009

87. Dilmac B, Yurt E, Aydin M, Kasarci I. Predictive relationship between humane values of adolescents cyberbullying and cyberbullying sensibility. Electron J Res Educ Psychol. (2016) 14:3–22. doi: 10.14204/ejrep.38.14123

88. Reed KP, Cooper RL, Nugent WR, Russell K. Cyberbullying: a literature review of its relationship to adolescent depression and current intervention strategies. J Hum Behav Soc Environ. (2016) 26:37–45. doi: 10.1080/10911359.2015.1059165

Keywords: cyberbullying, children, adolescents, globalization, risk factors, preventive measures

Citation: Zhu C, Huang S, Evans R and Zhang W (2021) Cyberbullying Among Adolescents and Children: A Comprehensive Review of the Global Situation, Risk Factors, and Preventive Measures. Front. Public Health 9:634909. doi: 10.3389/fpubh.2021.634909

Received: 29 November 2020; Accepted: 10 February 2021; Published: 11 March 2021.

Reviewed by:

Copyright © 2021 Zhu, Huang, Evans and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Wei Zhang, weizhanghust@hust.edu.cn

† These authors have contributed equally to this work and share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Teens and Cyberbullying 2022

Nearly half of u.s. teens have been bullied or harassed online, with physical appearance being seen as a relatively common reason why. older teen girls are especially likely to report being targeted by online abuse overall and because of their appearance, table of contents.

• Age and gender are related to teens’ cyberbullying experiences, with older teen girls being especially likely to face this abuse
• Black teens are about twice as likely as Hispanic or White teens to say they think their race or ethnicity made them a target of online abuse
• Black or Hispanic teens are more likely than White teens to say cyberbullying is a major problem for people their age
• Roughly three-quarters of teens or more think elected officials and social media sites aren’t adequately addressing online abuse
• Large majorities of teens believe permanent bans from social media and criminal charges can help reduce harassment on the platforms
• Acknowledgments
• Methodology

Pew Research Center conducted this study to better understand teens’ experiences with and views on bullying and harassment online. For this analysis, we surveyed 1,316 U.S. teens. The survey was conducted online by Ipsos from April 14 to May 4, 2022.

This research was reviewed and approved by an external institutional review board (IRB), Advarra, which is an independent committee of experts that specializes in helping to protect the rights of research participants.

Ipsos recruited the teens via their parents who were a part of its  KnowledgePanel , a probability-based web panel recruited primarily through national, random sampling of residential addresses. The survey is weighted to be representative of U.S. teens ages 13 to 17 who live with parents by age, gender, race, ethnicity, household income and other categories.

Here are the  questions used for this report , along with responses, and  its methodology .

While bullying existed long before the internet, the rise of smartphones and social media has brought a new and more public arena into play for this aggressive behavior.

Nearly half of U.S. teens ages 13 to 17 (46%) report ever experiencing at least one of six cyberbullying behaviors asked about in a Pew Research Center survey conducted April 14-May 4, 2022. 1

The most commonly reported behavior in this survey is name-calling, with 32% of teens saying they have been called an offensive name online or on their cellphone. Smaller shares say they have had false rumors spread about them online (22%) or have been sent explicit images they didn’t ask for (17%).

Some 15% of teens say they have experienced someone other than a parent constantly asking them where they are, what they’re doing or who they’re with, while 10% say they have been physically threatened and 7% of teens say they have had explicit images of them shared without their consent.

In total, 28% of teens have experienced multiple types of cyberbullying.

Defining cyberbullying in this report

This report measures cyberbullying of teens using six distinct behaviors:

• Offensive name-calling
• Spreading of false rumors about them
• Receiving explicit images they didn’t ask for
• Physical threats
• Constantly being asked where they are, what they’re doing, or who they’re with by someone other than a parent
• Having explicit images of them shared without their consent

Teens who indicate they have personally experienced any of these behaviors online or while using their cellphone are considered targets of cyberbullying in this report. The terms “cyberbullying” and “online harassment” are used interchangeably throughout this report.

Teens’ experiences with online harassment vary by age. Some 49% of 15- to 17-year-olds have experienced at least one of the six online behaviors, compared with 42% of those ages 13 to 14. While similar shares of older and younger teens report being the target of name-calling or rumor spreading, older teens are more likely than their younger counterparts (22% vs. 11%) to say someone has sent them explicit images they didn’t ask for, an act sometimes referred to as cyberflashing ; had someone share explicit images of them without their consent, in what is also known as revenge porn (8% vs. 4%); or been the target of persistent questioning about their whereabouts and activities (17% vs. 12%).

While there is no gender difference in having ever experienced online abuse, teen girls are more likely than teen boys to say false rumors have been spread about them. But further differences are seen when looking at age and gender together: 15- to 17-year-old girls stand out for being particularly likely to have faced any cyberbullying, compared with younger teen girls and teen boys of any age. Some 54% of girls ages 15 to 17 have experienced at least one of the six cyberbullying behaviors, while 44% of 15- to 17-year-old boys and 41% of boys and girls ages 13 to 14 say the same. These older teen girls are also more likely than younger teen girls and teen boys of any age to report being the target of false rumors and constant monitoring by someone other than a parent.

White, Black and Hispanic teens do not statistically differ in having ever been harassed online, but specific types of online attacks are more prevalent among certain groups. 2 For example, White teens are more likely to report being targeted by false rumors than Black teens. Hispanic teens are more likely than White or Black teens to say they have been asked constantly where they are, what they’re doing or who they’re with by someone other than a parent.

There are also differences by household income when it comes to physical threats. Teens who are from households making less than $30,000 annually are twice as likely as teens living in households making $75,000 or more a year to say they have been physically threatened online (16% vs. 8%).

Beyond those differences related to specific harassing behaviors, older teen girls are particularly likely to say they experience multiple types of online harassment. Some 32% of teen girls have experienced two or more types of online harassment asked about in this survey, while 24% of teen boys say the same. And 15- to 17-year-olds are more likely than 13- to 14-year-olds to have been the target of multiple types of cyberbullying (32% vs. 22%).

These differences are largely driven by older teen girls: 38% of teen girls ages 15 to 17 have experienced at least two of the harassing behaviors asked about in this survey, while roughly a quarter of younger teen girls and teen boys of any age say the same.

Beyond demographic differences, being the target of these behaviors and facing multiple types of these behaviors also vary by the amount of time youth spend online. Teens who say they are online almost constantly are not only more likely to have ever been harassed online than those who report being online less often (53% vs 40%), but are also more likely to have faced multiple forms of online abuse (37% vs. 21%).

These are some of the findings from a Pew Research Center online survey of 1,316 U.S. teens conducted from April 14 to May 4, 2022.

There are numerous reasons why a teen may be targeted with online abuse. This survey asked youth if they believed their physical appearance, gender, race or ethnicity, sexual orientation or political views were a factor in them being the target of abusive behavior online.

Teens are most likely to say their physical appearance made them the target of cyberbullying. Some 15% of all teens think they were cyberbullied because of their appearance.

About one-in-ten teens say they were targeted because of their gender (10%) or their race or ethnicity (9%). Teens less commonly report being harassed for their sexual orientation or their political views – just 5% each.

Looking at these numbers in a different way, 31% of teens who have personally experienced online harassment or bullying think they were targeted because of their physical appearance. About one-in-five cyberbullied teens say they were targeted due to their gender (22%) or their racial or ethnic background (20%). And roughly one-in-ten affected teens point to their sexual orientation (12%) or their political views (11%) as a reason why they were targeted with harassment or bullying online.

The reasons teens cite for why they were targeted for cyberbullying are largely similar across major demographic groups, but there are a few key differences. For example, teen girls overall are more likely than teen boys to say they have been cyberbullied because of their physical appearance (17% vs. 11%) or their gender (14% vs. 6%). Older teens are also more likely to say they have been harassed online because of their appearance: 17% of 15- to 17-year-olds have experienced cyberbullying because of their physical appearance, compared with 11% of teens ages 13 to 14.

Older teen girls are particularly likely to think they have been harassed online because of their physical appearance: 21% of all 15- to 17-year-old girls think they have been targeted for this reason. This compares with about one-in-ten younger teen girls or teen boys, regardless of age, who think they have been cyberbullied because of their appearance.

A teen’s racial or ethnic background relates to whether they report having been targeted for cyberbullying because of race or ethnicity. Some 21% of Black teens report being made a target because of their race or ethnicity, compared with 11% of Hispanic teens and an even smaller share of White teens (4%).

There are no partisan differences in teens being targeted for their political views, with 5% of those who identify as either Democratic or Republican – including those who lean toward each party – saying they think their political views contributed to them being cyberbullied.

In addition to measuring teens’ own personal experiences with cyberbullying, the survey also sought to understand young people’s views about online harassment more generally.

The vast majority of teens say online harassment and online bullying are a problem for people their age, with 53% saying they are a major problem. Just 6% of teens think they are not a problem.

Certain demographic groups stand out for how much of a problem they say cyberbullying is. Seven-in-ten Black teens and 62% of Hispanic teens say online harassment and bullying are a major problem for people their age, compared with 46% of White teens. Teens from households making under $75,000 a year are similarly inclined to call this type of harassment a major problem, with 62% making this claim, compared with 47% of teens from more affluent homes. Teen girls are also more likely than boys to view cyberbullying as a major problem.

Views also vary by community type. Some 65% of teens living in urban areas say online harassment and bullying are a major problem for people their age, compared with about half of suburban and rural teens.

Partisan differences appear as well: Six-in-ten Democratic teens say this is a major problem for people their age, compared with 44% of Republican teens saying this.

In recent years, there have been several initiatives and programs aimed at curtailing bad behavior online, but teens by and large view some of those behind these efforts – including social media companies and politicians – in a decidedly negative light.

According to teens, parents are doing the best of the five groups asked about in terms of addressing online harassment and online bullying, with 66% of teens saying parents are doing at least a good job, including one-in-five saying it is an excellent job. Roughly four-in-ten teens report thinking teachers (40%) or law enforcement (37%) are doing a good or excellent job addressing online abuse. A quarter of teens say social media sites are doing at least a good job addressing online harassment and cyberbullying, and just 18% say the same of elected officials. In fact, 44% of teens say elected officials have done a poor job addressing online harassment and online bullying.

Teens who have been cyberbullied are more critical of how various groups have addressed online bullying than those who haven’t

Teens who have experienced harassment or bullying online have a very different perspective on how various groups have been handling cyberbullying compared with those who have not faced this type of abuse. Some 53% of teens who have been cyberbullied say elected officials have done a poor job when it comes to addressing online harassment and online bullying, while 38% who have not undergone these experiences say the same (a 15 percentage point gap). Double-digit differences also appear between teens who have and have not been cyberbullied in their views on how law enforcement, social media sites and teachers have addressed online abuse, with teens who have been harassed or bullied online being more critical of each of these three groups. These harassed teens are also twice as likely as their peers who report no abuse to say parents have done a poor job of combatting online harassment and bullying.

Aside from these differences based on personal experience with cyberbullying, only a few differences are seen across major demographic groups. For example, Black teens express greater cynicism than White teens about how law enforcement has fared in this space: 33% of Black teens say law enforcement is doing a poor job when it comes to addressing online harassment and online bullying; 21% of White teens say the same. Hispanic teens (25%) do not differ from either group on this question.

Teens have varying views about possible actions that could help to curb the amount of online harassment youth encounter on social media.

While a majority of teens say each of five possible solutions asked about in the survey would at least help a little, certain measures are viewed as being more effective than others.

Teens see the most benefit in criminal charges for users who bully or harass on social media or permanently locking these users out of their account. Half of teens say each of these options would help a lot in reducing the amount of harassment and bullying teens may face on social media sites.

About four-in-ten teens think that if social media companies looked for and deleted posts they think are bullying or harassing (42%) or if users of these platforms were required to use their real names and pictures (37%) it would help a lot in addressing these issues. The idea of forcing people to use their real name while online has long existed and been heavily debated: Proponents see it as a way to hold bad actors accountable and keep online conversations more civil , while detractors believe it would do little to solve harassment and could even  worsen it .

Three-in-ten teens say school districts monitoring students’ social media activity for bullying or harassment would help a lot. Some school districts already use digital monitoring software to help them identify worrying student behavior on school-owned devices , social media and other online platforms . However, these programs have been met with criticism regarding privacy issues , mixed results and whether they do more harm than good .

Having personally experienced online harassment is unrelated to a teen’s view on whether these potential measures would help a lot in reducing these types of adverse experiences on social media. Views do vary widely by a teen’s racial or ethnic background, however.

Black or Hispanic teens are consistently more optimistic than White teens about the effectiveness of each of these measures.

Majorities of both Black and Hispanic teens say permanently locking users out of their account if they bully or harass others or criminal charges for users who bully or harass on social media would help a lot, while about four-in-ten White teens express each view.

In the case of permanent bans, Black teens further stand out from their Hispanic peers: Seven-in-ten say this would help a lot, followed by 59% of Hispanic teens and 42% of White teens.

• It is important to note that there are various ways researchers measure youths’ experiences with cyberbullying and online harassment. As a result, there may be a range of estimates for how many teens report having these experiences. In addition, since the Center last polled on this topic in 2018, there have been changes in how the surveys were conducted and how the questions were asked. For instance, the 2018 survey asked about bullying by listing a number of possible behaviors and asking respondents to “check all that apply.” This survey asked teens to answer “yes” or “no” to each item individually. Due to these changes, direct comparisons cannot be made across the two surveys. ↩
• There were not enough Asian American teen respondents in the sample to be broken out into a separate analysis. As always, their responses are incorporated into the general population figures throughout the report. ↩

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9 facts about bullying in the u.s., life on social media platforms, in users’ own words, after musk’s takeover, big shifts in how republican and democratic twitter users view the platform, the behaviors and attitudes of u.s. adults on twitter, most popular.

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Horror Homeroom Special Issue #9: Body Horror

Though the term was coined in 1986, ‘body horror’ dates back to the beginnings of Gothic literature—Shelley’s Frankenstein (1818); Stevenson’s The Strange Case of Dr. Jekyll and Mr. Hyde (1886)—and extends into contemporary fiction, film, and new media. From seminal works including David Cronenberg’s The Fly (1986) to contemporary zombie films and portrayals of the digital-corporeal connection, as in the Unfriended franchise and Jane Schoenbrun’s recent  I Saw the TV Glow , embodiment remains central to the horror genre. Mirroring the porousness of the body itself, the category evades compartmentalization and definition. 

This special issue will contend with horror’s bodies in all their transgressive fluidity. We are open to essays exploring any texts that could broadly be considered ‘body horror,’ including fiction, film, and new media. We also welcome a variety of theoretical approaches and disciplinary methods. Lastly, since body horror is a global phenomenon, we hope to put together an issue that makes international connections. 

Potential topics include (but are not limited to): 

medical experimentation

shape-shifting/transformation

cannibalism

identity and embodiment 

biopolitics and necropolitics 

digital bodies 

posthumanism

key directors (Cronenberg, Ducournau, Soska sisters, etc.) 

body horror and pornography 

New Extremity films 

pregnancy/reproduction

environmental impacts on the body 

the role of camp and humor

torture porn

Please send an abstract of no more than 500 words along with a brief bio to Elizabeth Erwin ( [email protected] ), Lauren Gilmore ( [email protected] ), and Dawn Keetley ( [email protected] ) by August 18, 2024. We will select essays to include in the special issue within two-three weeks and notify everyone who submitted an abstract. Completed essays, which will be limited to 2,500 words, will be due by October 14, 2024, and should be written for a general audience. We welcome all questions and inquiries! 

Horror Homeroom’s special issues consist of relatively short (2,500 word) well-researched articles that are written for general and academic audiences. They are carefully reviewed by the editors.

Proposed timeline:

Abstracts due: August 18, 2024

Acceptances out: September 2, 2024

Essays due: October 14, 2024

Selected Bibliography: 

Aldana Reyes, Xavier. 2014. Body Gothic: Corporeal Transgression in Contemporary Literature and Horror Film, University of Wales Press.

- - - . 2024. Contemporary Body Horror, forthcoming from Cambridge Elements.

Anderson, Jill E. 2023. “Her Body and Other Ghosts: Embodied Horror in the Works of Shirley Jackson and Carmen Maria Machado.” Monstrum 6 (2): 31-50.

Arnold, Sarah. 2013. Maternal Horror Film: Melodrama and Motherhood, Springer. 

Brophy, Philip. 1986. “Horrality: The Textuality of the Contemporary Horror Film.” Screen 27 (1): 2–13. 

Cruz, Ronald Allan Lopez. 2012. “Mutations and Metamorphoses: Body Horror is Biological Horror.” Journal of Popular Film and Television 40: 160–8. 

Diffrient, David Scott. 2023. Body Genre: Anatomy of the Horror Film, University Press of Mississippi.

Folio, Jessica and Holly Luhnig, eds. 2014. Body Horror and Shapeshifting: A Multidisciplinary Exploration, Inter-Disciplinary Press.

Harrington, Erin. 2018. Women, Monstrosity, and Horror Film Gynaehorror, Routledge. 

Huckvale, David. 2020. Terrors of the Flesh: The Philosophy of Body Horror in Film, McFarland.

Wasson, Sara. 2020. Transplantation Gothic: Tissue Transfers in Literature, Film, and Medicine, Manchester University Press. 

Wald, Priscilla. 2008. Contagious: Cultures, Carriers and the Outbreak Narrative, Duke University Press. 

Williams, Linda. 1991. “Film Bodies: Gender, Genre, and Excess.” Film Quarterly 44 (4): 2–13.

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Self-Esteem and Adolescent Bullying/Cyberbullying and Victimization/Cybervictimization Behaviours: A Person-Oriented Approach

Anna l. palermiti.

1 Department of Cultures, Education and Society, University of Calabria, Rende, Italy

Maria G. Bartolo

Pasquale musso.

2 Department of Educational Sciences, Psychology, Communication, University of Studies of Bari “Aldo Moro”, Bari, Italy

Rocco Servidio

Angela costabile.

Although previous studies seemed to recognize negative associations between self-esteem and bullying/cyberbullying and victimization/cybervictimization behaviours, the findings are controversial. The current study tried to shed light on this issue by using a person-oriented approach among Italian adolescents. Participants included 936 students aged 13-16 years. Different domains of self-esteem and bullying/cyberbullying and victimization/cybervictimization behaviour during the previous 2-3 months were assessed through a self-administered questionnaire. The results suggested four self-esteem profiles, i.e., school/family-oriented, consistently high, self-derogation, and body/peer-oriented. Students in the consistently high self-esteem profile seemed to be more protected against bullying/cyberbullying and victimization/cybervictimization behaviours compared to those in the self-derogation profile. The findings showed that among adolescents there is a degree of heterogeneity in the self-esteem domain associated with different levels of bullying/cyberbullying and victimization/cybervictimization behaviour. This suggests that different domains of self-esteem and their interdependencies play a crucial role during adolescence, with consequences also in terms of diverse patterns of active and passive aggressive behaviour.

Bullying and cyberbullying are two social phenomena that involve many children and youth. Both refer to repeatedly intentional, systematic, and aggressive behaviours manifested by an individual or a group of peers against a victim in a context of power imbalance ( Smith, 2014 ). However, bullying concerns a vis-a-vis relationship, while cyberbullying pertains to the use of digital devices.

Bullying occurs in two different forms: direct (e.g., physical attacks or verbal offences) and/or indirect (e.g., social exclusion or the spreading of malicious rumours). It revolves around persons who play a specific role in a group, such as bully, victim, reinforcer of the bully, assistant of the bully, defender of the victim, and outsider ( Salmivalli, Lagerspetz, Björkqvist, Österman, & Kaukiainen, 1996 ). Cyberbullying maintains some of the main characteristics of traditional bullying such as the intentionality and the imbalance of power, but it occurs in cyberspace or digital environments (e.g., social networks, chats, blogs), thus swiftly reaching a far wider audience. Like bullying, cyberbullying can manifest itself in different forms such as online harassment, denigration, happy slapping, trickery, and impersonation, and it is characterized by anonymity, stimulating more disinhibited behaviours and concealing cyberbullies’ identity from their victims ( Kokkinos & Antoniadou, 2019 ; Lei et al., 2020 ).

Most researchers use the terms bullying and cyberbullying to denote both the active and passive sides of this aggressive/violent behaviour, but some prefer to adopt a clearer distinction by using bullying/cyberbullying to identify the active side of the phenomenon and victimization/cybervictimization to identify the passive one (e.g., Chen & Wei, 2011 ; Rodríguez-Enríquez, Bennasar-Veny, Leiva, Garaigordobil, & Yañez, 2019 ). This conceptual distinction is also adopted in the present study.

A growing number of studies on aggressive behaviours have investigated risk and protective factors related to bullying/cyberbullying and victimization/cybervictimization and have identified self-esteem as one of the most central of these factors (see, for example, Beltrán-Catalán, Zych, Ortega-Ruiz, & Llorent, 2018 ; Palermiti, Servidio, Bartolo, & Costabile, 2017 ; Tsaousis, 2016 ). Self-esteem is the product of a lifelong developmental process (see Mroczek & Little, 2014 ). It determines the overall assessment of a person’s value and can be considered an essential component of well-being. From this perspective, self-esteem may acquire a fundamental motivational function that can either activate or inhibit certain aspects of a person’s developmental trajectories ( Harter & Whitesell, 2003 ), with high levels of self-esteem operating as protective factors and low levels increasing vulnerability to peer aggression and mental health problems ( Ybrandt & Armelius, 2010 ).

However, the nature of the relation between self-esteem, on the one hand, and bullying/cyberbullying and victimization/cybervictimization, on the other, is still controversial ( Lei et al., 2020 , for a meta-analysis). In most cases, findings have highlighted a negative relation between self-esteem and victimization experiences, with high levels of self-esteem serving as protective factors (for the latest meta-analysis, see Tsaousis, 2016 ). Other studies have shown that both victims ( Kowalski & Limber, 2013 ) and bullies (e.g., Jankauskiene, Kardelis, Sukys, & Kardeliene, 2008 ) have low levels of self-esteem. Other studies still have found bullies to have high levels of self-esteem (e.g., Salmivalli, Kaukiainen, Kaistaniemi, & Lagerspetz, 1999 ) or have found no association between self-esteem and bullying ( Olweus, 1993 ; Pearce &Thompson, 1998 ). Research focusing on cyberbullying and cybervictimization has yielded similar controversial results ( Cénat et al., 2014 ; Kowalski & Limber, 2013 ; Palermiti et al., 2017 ; Patchin & Hinduja, 2010 ).

Different explanations have been adduced to account for these results. Two of the main explanatory hypotheses are the low self-esteem hypothesis ( Donnellan, Trzesniewski, Robins, Moffitt, & Caspi, 2005 ), suggesting that aggressive behaviours are an expression of youths’ low self-esteem, and the disputed self-esteem hypothesis ( Baumeister, Bushman, & Campbell, 2000 ), arguing that children and youths adopt bullying behaviour because others threaten their self-esteem. However, these hypotheses do not account for the controversial nature of the results provided by the literature.

An alternative explanation is related to the approach that most studies have used to date. Most researchers have investigated the relation between self-esteem and bullying/cyberbullying and victimization/cybervictimization by using a variable-oriented approach with the self-esteem variable as the basic conceptual and analytic unit. As a consequence, they have considered the properties of the samples to be internally homogeneous. In practice, the variable-oriented approach, which analyses associations between variables based on aggregate data and average series across individuals, can misrepresent both the heterogeneity of the groups and individual patterns of development ( von Eye & Bergman, 2003 ; von Eye & Bogat, 2006 ) and it is likely that it does not adequately reflect reality ( Morin, Bujacz, & Gagné, 2018 ). Thus, the variable-centred approach can provide important information on the average differences between people, but it is often inaccurate and ignores variations in individuals’ experiences ( Moses & Williford, 2017 ). On the other hand, a person-oriented approach ( Bergman & Trost, 2006 ) has the advantage of allowing individuals to be classified into meaningful distinct groups based on their response patterns, whereby individuals within a group will be more similar than individuals across different groups ( Moreira, Inman, Cloninger, & Cloninger, 2021 ). This makes it possible to address the multifaceted, varied nature of individual experiences. Despite these advantages, few studies so far have used a person-oriented approach to investigate how bullying/cyberbullying and victimization/cybervictimization behaviours are related to different self-esteem profile groups. As mentioned, this perspective may explain the current controversial findings in the field and require more investigation.

One reason for the underuse of the person-oriented approach may be the extensive use of global measures of self-esteem according to an exclusive low ↔ high unidimensional model. However, the literature shows how it is possible to conceptualize self-esteem not as a global concept but as a multidimensional construct including different domains. DuBois, Felner, Brand and George (1999) stated that self-evaluations find correspondence with the major settings or contexts of life, such as family, school, peer relations, physical appearance, and sports/athletics. This conceptualization was also supported by other studies (e.g., Brighi, Guarini, Melotti, Galli, & Genta, 2012 ; Harter, 2012 ) that similarly considered different domains of self-esteem, such as the educational, socio-relational, physical, and athletic. All these domains are particularly important during adolescent development. Indeed, adolescence is one of the most sensitive periods of life in terms of self-esteem processes, owing to the rapid physical, psychological, and social changes that are normally involved in this developmental phase. New relationships with family, school, and peers, and new issues related to peer acceptance and physical appearance make adolescents more susceptible to experiences and views that can largely influence their self-image (see Twenge & Campbell, 2001 ) and, consequently, their personal and social assets (e.g., skills and competencies). In view of this, since bullying/cyberbullying and victimization/cybervictimization phenomena may originate from diverse ecological conditions in different contexts ( Bartolo, Palermiti, Servidio, Musso, & Costabile, 2019 ), it may be critical to investigate how different domains of self-esteem (e.g., family, school, peer relations) can be specifically linked with diverse forms of active and passive aggressive behaviour.

Starting from such premises, this study utilized a person-oriented approach to examine how bullying/cyberbullying and victimization/cybervictimization levels differed across different self-esteem profiles. To obtain such profiles we analysed similarities and differences among adolescent students with respect to the connections between self-esteem domain-related variables ( DuBois et al, 1999 ). This enabled us, for example, to assign certain individuals a positive self-image in one domain but a negative self-image in another, without necessarily damaging their overall self-esteem level ( von Soest, Wichstrøm, & Kvalem, 2016 ; Wichstrøm, & von Soest, 2016 ), thereby improving our understanding of adolescent aggressive behaviour development.

As already mentioned, the present study aimed to examine how self-esteem is associated with bullying/cyberbullying and victimization/cybervictimization behaviours in Italian adolescent students. To better account for the heterogeneity of individual developments, we adopted a person-oriented perspective ( Bergman & Trost, 2006 ; Moreira et al., 2021 ) that focused on the identification of self-esteem profiles based on scores obtained by using a multi-domain (peer relations, school, family, physical appearance, sports/athletics) self-esteem measure ( DuBois et al., 1999 ). Then, we assessed how self-esteem profiles were associated with bullying/cyberbullying and victimization/cybervictimization behaviours. We were generally guided by the hypothesis that self-esteem profiles characterized by high levels of self-esteem in diverse domains may mitigate involvement in bullying/cyberbullying and victimization/cybervictimization behaviours.

In exploring this hypothesis, gender and age were taken into account. These demographic variables have been shown to be differently associated with both bullying/cyberbullying and victimization/cybervictimization behaviours. In terms of gender differences, several studies have shown that males are usually more involved in bullying than females, especially when considering physical forms of bullying as opposed to the relational or verbal ones ( Besag, 2006 ; Crick & Grotpeter, 1995 ). No major differences have been found with reference to victimization. Regarding cyberbullying/cybervictimization, studies have shown controversial findings: some studies have reported no differences ( Hinduja & Patchin, 2007 ; Williams & Guerra, 2007 ), while others have found that males are more involved as cyberbullies ( Li, 2006 ) and females as cybervictims ( Guo, 2016 ; Calvete, Orue, Estévez, Villardón, & Padilla, 2010 ). In terms of age differences, a recent study ( Tsaousis, 2016 ) reported that bullying tends to increase during childhood and early adolescence and to decline slightly only during the late adolescent years, while victimization decreases in the passage from childhood to adolescence. Cyberbullying and cybervictimization seem to follow different developmental patterns, with an increase over the years of secondary school ( Smith & Slonje, 2010 ), probably due to easier access to digital communication tools and the Internet among older students.

Participants

Participants included 980 ninth- and tenth-grade students recruited from 5 state high schools in southern Italy. Only a small and acceptable proportion of them ( n = 44; 4.5%) had missing information pertaining to one or more of the study variables. These participants were excluded from the analyses. Our final sample thus consisted of 936 adolescents (males = 48.6% and females = 51.4%) aged 13 to 16 years ( M = 14.49, SD = 0.50). The majority of participants were Italian (93.0%), had cohabiting parents (87.8%), and came from middle-class backgrounds (76.4%) – less than 2% of the adolescents’ parents had an elementary school education and less than 22% had a university or postgraduate education.

The whole procedure was performed in accordance with the Italian Psychological Association’s ethical principles ( http://www.aipass.org/node/11560 ). Participants’ parents were informed – via a letter from the principal of each school – about the purpose of the research, the voluntary nature of participation, and the anonymity of the responses. Parents provided written informed consent to their son’s or daughter’s participation. Furthermore, adolescent participants were required to submit a signed assent form in order to take part in the study. Participants had about 30 mins to complete an anonymous online survey during class time and could withdraw at any time.

Socio-Demographics

Participants were asked to indicate their gender, age, ethnicity, and socio-economic status (SES), as well as their parents’ marital status and education.

Self-Esteem Questionnaire

The 31-item self-report Italian version of the Self-Esteem Questionnaire (SEQ; DuBois et al., 1999 ; Melotti & Passini, 2002 ) was used to assess different domains of self-esteem: peer relations (seven items; e.g., “I am as good as I want to be at making new friends”), school (seven items; e.g., “I feel OK about how good of a student I am”), family (eight items; e.g., “I get along with my family as well as I would like to”), physical appearance (four items; e.g., “I like my body just the way it is”), sports/athletics (five items; e.g., “I am as good at sports/physical activities as I want to be”). Items were scored on a Likert-type scale ranging from 1 ( totally disagree ) to 4 ( totally agree ). For each subscale, a mean score was computed. Cronbach’s alpha values for peer relations (α = .88), school (α = .87), family (α = .77), physical appearance (α = .87), and sports/athletics (α = .87) were consistent with prior research.

Florence Bullying/Victimization Scales

The Florence Bullying and Victimization Scales (FBVSs; Palladino, Nocentini & Menesini, 2016 ) were used to assess traditional bullying and victimization behaviours during the previous 2–3 months. Each scale contains 10 items (e.g., “I called someone names” for bullying and “I have been called names” for victimization). Both scales consist of three subscales: physical behaviours, verbal behaviours, and indirect-relational behaviours. Previous research supported the use of the FBVSs as second order measures to obtain global scores for bullying and victimization. Items were scored on a Likert-type scale ranging from 1 ( never ) to 5 ( several times a week ). For each scale, a mean score was computed. Cronbach’s alpha values for physical (α = .82), verbal (α = .77), indirect-relational (α = .68), and global bullying (α = .85) scores as well as physical (α = .62), verbal (α = .69), indirect-relational (α = .65) and global victimization (α = .78) scores were consistent with prior research.

Florence CyberBullying/CyberVictimization Scales

The Florence CyberBullying/CyberVictimization Scales (FCBVSs; Palladino et al., 2016 ) were used to assess cyberbullying and cybervictimization behaviours during the previous 2–3 months. Each scale contains 14 items (e.g., “I have stolen personal data, such as images and photos, in order to reuse them” for cyberbullying and “Personal data, such as images and photos, have been stolen from me in order to reuse them” for cybervictimization). Both scales consist of four subscales: written-verbal, visual, impersonation, and exclusion. Previous research also supported the use of the FCBVSs as second order measures to obtain global scores for cyberbullying and cybervictimization. Items were scored on a Likert-type scale ranging from 1 ( never ) to 5 ( several times a week ). For each scale, a mean score was computed. Cronbach’s alpha values for written-verbal (α = .88), visual (α = .86), impersonation (α = .91), exclusion (α = .83), and global cyberbullying (α = .95) scores as well as written-verbal (α = .79), visual (α = .72), impersonation (α = .80), exclusion (α = .75), and global cybervictimization (α = .90) scores were consistent with prior research.

Data Analysis

We followed three main steps to carry out the data analysis. First, we computed descriptive statistics for the key study variables including means and standard deviations, skewness and kurtosis indices, the minimum and maximum values of standardized scores, and Pearson’s bivariate correlations. This allowed verification of the univariate normality of the distributions. Whenever required, non-normally distributed variables were transformed to improve normality and extreme outliers. Furthermore, the Mahalanobis distance and Mardia’s multivariate kurtosis coefficient were used to test multivariate normality and identify other potential multivariate outliers. Then, the final descriptive statistics for the study variables were computed.

Second, to identify self-esteem profiles of adolescent students, we conducted a cluster analysis based on the standardized scores of SEQ subscales (peer relations, school, family, physical appearance, and sports/athletics). Specifically, at an initial step, we carried out agglomerative hierarchical cluster analyses, using Ward’s method based on the squared Euclidean distance ( Aldenderfer & Blashfield, 1984 ) and examining solutions from two to six clusters, to determine the most appropriate number of clusters. The criteria used to choose this number included the theoretical meaningfulness of each cluster, parsimony, and explanatory power. With regard to explanatory power, the cluster solution had to explain at least 26% of the variance in each of the SEQ dimensions (see Cohen, 1988 ). Then, the study participants were grouped by K -means cluster analysis procedures and the standardized mean values of the SEQ grouping variables describing the characteristics of each identified cluster were calculated. To check the validity and stability of the solution, a multivariate analysis of variance (MANOVA) on the five SEQ dimensions (the dependent variables) by the cluster groups (the independent variable) was performed and the replicability of the solution was tested. As indicated by Breckenridge (2000) , data were randomly divided into two subsets (A and B) and cluster analyses were newly conducted for each of them. Then, subset B was classified into clusters according to the cluster centres derived from subset A and the agreement between the two subset B solutions was computed using Cohen’s kappa, with higher agreement indicative of a more stable cluster solution.

Third, to compare the bullying/cyberbullying and victimization/cybervictimization variables in relation to the self-esteem profile groups, we carried out a MANOVA with self-esteem profiles, gender, and age (dummy coded: 0 = 13–14 years; 1 = 15–16 years) as independent variables and bullying/cyberbullying and victimization/cybervictimization behaviours as dependent variables.

Descriptive Statistics

We initially computed descriptive statistics. As is commonly the case in the investigated field of study ( Palladino et al., 2016 ), bullying/cyberbullying and victimization/cybervictimization variables were strongly non-normal (see Tabachnick & Fidell, 2013 ), with skewness values ranging from 3.49 to 8.99, kurtosis values ranging from 19.98 to 98.53, and the maximum values of standardized scores ranging from 9.46 to 14.26. For these reasons, a transformation was applied for all these variables using the two-step approach to transform the non-normally distributed variables suggested by Templeton (2011) , as the best choice. After re-calculating descriptive statistics for the transformed variables, the new distributions showed acceptable values (see Table 1 ). Furthermore, the multivariate inspection of the data revealed twenty-six (2.8%) potential multivariate outliers. However, after performing analyses with or without these cases, we found no substantial effect on the pattern of results. Thus, we retained these cases in the final sample.

Note. Values are of standardized scores and bivariate correlations for the key study variables in their original or final transformed version ( n = 936).

*p < .05. ** p < .01. *** p < .001.

Cluster Analysis

Identification of the optimal number of clusters.

Based on the initial agglomerative hierarchical cluster analyses and the a priori criteria, a four-cluster solution was found to be the most acceptable. On the one hand, the solution with two or three clusters explained less than 26% of the variability in at least one of the SEQ specific dimensions. On the other hand, solutions with five or six clusters violated the principle of parsimony, because they included clusters that represented slight variations compared to the four most interpretable clusters and did not have a clear theoretical meaning.

K-Means Cluster Analysis and Description of Self-Esteem Profiles

After establishing the most appropriate number of clusters, the participants were clustered into four groups by K -means cluster analysis. Figure 1 shows the self-esteem profiles obtained. The first cluster ( n = 223, 23.83% of the sample) was composed of students who scored moderately high on the self-esteem domains of school and family, moderately low on the self-esteem domain of peer relations, and low on the self-esteem domains of physical appearance and sports/athletics. The second cluster ( n = 199, 21.26% of the sample) consisted of students scoring high on all self-esteem domains of peer relations, school, family, physical appearance, and sports/athletics. The third cluster ( n = 217, 23.18% of the sample) comprised primarily students with low scores on all five self-esteem domains. The fourth cluster ( n = 297, 31.73% of the sample) consisted of students scoring moderately high on the self-esteem domains of physical appearance, sports/athletics, and peer relations, and moderately low on the self-esteem domain of family and school. Thus, we found, in sequence, clusters representing school/family-oriented, consistently high, self-derogation, and body/peer-oriented self-esteem profiles ( DuBois et al., 1999 ).

Note. For the self-esteem domains of peer relations, school, family, physical appearance, and sports/athletics across the four self-esteem profiles.

Validity and Stability of the Cluster Solution

The MANOVA computed on the five self-esteem domain variables revealed a significant multivariate effect of the cluster solution, Wilks’ Lambda = .12, F (15, 2562) = 197.19, p < .001, η 2 = .51, indicating that about 51% of the variability was accounted for by group differences among the four clusters. Also, follow-up ANOVAs indicated that the four-cluster solution explained good percentages of variance for each variable related to the diverse self-esteem domains (41% of variability in peer relations, 46% in school, 49% in family, 50% in physical appearance, and 44% in sports/athletics). The replicability procedure indicated that the four-cluster solution was the best for both random subsets, A and B, and that the agreement between the two solutions of subset B was .73, indicating a substantial level of reliability.

MANOVA on Bullying/Cyberbullying and Victimization/Cybervictimization Variables

Multivariate test statistic.

The MANOVA on the bullying/cyberbullying and victimization/cybervictimization variables resulted in a significant multivariate effect of the self-esteem profiles, Wilks’ Lambda = .90, F (12, 2424) = 8.48, p < .001, η 2 = .04, and gender, Wilks’ Lambda = .96, F (4, 916) = 10.54, p < .001, η 2 = .04. None of the other main effects and two- or three-way interactions were statistically significant.

Follow-Up Univariate Analyses

Follow-up univariate analyses indicated that (a) levels of bullying/cyberbullying and victimization/cybervictimization behaviour differed significantly across self-esteem profiles, and (b) bullying/cyberbullying (but not victimization/cybervictimization) differed significantly across gender. Specifically, group comparisons based on Tukey’s Honestly Significant Difference (HSD) post hoc analyses for the four self-esteem profiles (see Table 2 ) and on Bonferroni adjustments for gender (see Table 3 ) revealed that:

Note. Comparisons based on Tukey’s tests for the four self-esteem profiles on bullying/cyberbullying and victimization/cybervictimization behaviours. Different subscripts (a, b, c, or d) within a row indicate significant differences ( p < .05) between profile group means.

*** p < .001.

Note. Comparisons based on Bonferroni’s adjustments for gender on bullying/cyberbullying and victimization/cybervictimization behaviours. Different subscripts (a and b) within a row indicate significant differences ( p < .05) between profile group means.

* p < .05. *** p < .001.

• Students in the self-derogation profile scored significantly higher on all bullying/cyberbullying (mean scores 1.31 and 1.15, respectively) and victimization/cybervictimization (1.38 and 1.19) behaviours than those in the other profiles (≤ 1.20 for bullying, ≤ 1.10 for cyberbullying, ≤ 1.28 for victimization, and ≤ 1.12 for cybervictimization), except for the comparison with the students in the body/peer-oriented profile in relation to bullying (1.31 vs. 1.25);
• Students in the consistently high self-esteem profile scored significantly lower on victimization/cybervictimization behaviours (1.12 and 1.04) than those in the other profiles (≥ 1.20 for victimization, and ≥ 1.11 for cybervictimization), while they scored similarly on bullying/cyberbullying behaviours compared to those in the school/family-oriented (1.20 vs. 1.19 for bullying and 1.06 vs. 1.08 for cyberbullying) and body/peer-oriented profiles (1.20 vs. 1.15 for bullying and 1.06 vs. 1.10 for cyberbullying);
• Students in the body/peer-oriented profile scored significantly lower on victimization than those in the school/family-oriented profile (1.20 vs. 1.28), while both groups scored similarly on bullying/cyberbullying (1.25 vs. 1.19 for bullying and 1.10 vs. 1.08 for cyberbullying) and cybervictimization (1.11 vs. 1.12) behaviours;
• Male students scored significantly higher on bullying/cyberbullying behaviours than female students (1.31 vs. 1.17 for bullying and 1.11 vs. 1.08 for cyberbullying), while both groups scored similarly on victimization/cybervictimization behaviours (1.25 vs. 1.24 for victimization and 1.11 vs. 1.12 for cybervictimization).

The main purpose of the current study was to examine the association of self-esteem profiles with bullying/cyberbullying and victimization/cybervictimization behaviours in adolescence. Differently from most previous studies, the added value was to apply a person-oriented approach to characterize our participants based on different domains of self-esteem ( DuBois et al., 1999 ). This enabled us to better consider their heterogeneity, by identifying different subgroups of participants with specific configurations of self-esteem. Results indicated that four different self-esteem profiles (i.e., school/family-oriented, consistently high, self-derogation, and body/peer-oriented) could be extracted and highlighted substantial differences between these profiles in terms of behavioural levels for bullying/cyberbullying and victimization/cybervictimization.

Findings on self-esteem profiles were largely consistent with those of previous studies (e.g., DuBois et al., 1999 ; Salmivalli et al., 1999 ), reporting both consistently high and self-derogation self-esteem profiles. The school/family-oriented and the body/peer-oriented profiles were theoretically meaningful and interpretable. From a developmental-ecological perspective, school, family, body-image, and peer relations are highly significant domains in adolescents’ lives (e.g., Ricciardelli & McCabe, 2001 ). Moreover, these domains may be considered interdependent across settings ( DuBois et al., 1999 ), with specific experiences and views in one domain (e.g., family or peer relations) tending to have corresponding impacts on those associated with related domains (e.g., school or body-image). Furthermore, such experiences and views may be more prevalent in one area of life (e.g., relations with adults in the family and at school) than another depending on the personal and environmental context in which adolescents are developing.

With regard to the associations between self-esteem profiles and bullying/cyberbullying and victimization/cybervictimization behaviours, the results showed that students classified in the self-derogation profile seemed to be more at risk of being involved in both bullying/cyberbullying and victimization/cybervictimization behaviours compared to those in the other profiles. This may complement findings in recent previous studies showing that lower levels of self-esteem are associated with higher risks of bullying and victimization ( Tsaousis, 2016 ). One exception is the non-significant difference between students in the self-derogation and body/peer-oriented profiles with respect to bullying. The emphasis on body- and peer-oriented self-evaluation domains may significantly increase students’ concerns about their concrete adaptive social experiences ( DuBois et al., 1999 ), thereby increasing the risk of involvement in problematic behaviours, like bullying.

Students in the consistently high self-esteem profile seemed to be more protected against bullying/cyberbullying and victimization/cybervictimization behaviours compared to those in the self-derogation profile. This seems like the other side of the coin with respect to what was already mentioned above. Also, adolescents in the consistently high self-esteem profile seemed to be more protected against victimization/cybervictimization behaviours compared to those in the school/family- and body/peer-oriented profiles, while there were no significant differences in terms of bullying/cyberbullying behaviours. The synergistic interactions between the self-esteem domains in the consistently high profile ( DuBois et al., 1999 ) may have the potential of considerably enriching personal and social skills and strategies that safeguard against situations of victimization. However, this could be only partially true and associated with specific skills and strategies with regard to school/family- or body/peer-oriented profiles. Thus, it appears that, in terms of reducing bullying/cyberbullying behaviours, it is important to establish uniform patterns of self-evaluation in at least one specific area (i.e., school and family or body-image and peer relations), if not all of them, as this would help prevent the expression of greater levels of aggression and antisocial behaviour as in the self-derogation profile.

Generally, students in the school/family-oriented profile presented the same levels of bullying/cyberbullying and cybervictimization behaviour as students in the body/peer-oriented profile, but the latter adolescents seemed to be more protected against victimization behaviours than the former. The emphasis on body- and peer-oriented domains of self-evaluation may increase—as already specified above—the risk of involvement in bullying behaviours but, at the same time, it may also facilitate the development of strategies to reduce victimization behaviours and events.

The findings were also examined in the light of demographic variables such as gender and age as well as their interactions with the self-esteem profiles. Only gender showed any significant direct influence. Specifically, boys reported higher levels of bullying/cyberbullying than girls. This is consistent with several studies (e.g., Li, 2006 ; Olweus, 1993 ). However, no differences emerged with regard to victimization/cybervictimization. This result is also in line with previous studies (e.g., Williams & Guerra, 2007 ), although other researchers found that girls are more involved in victimization/cybervictimization behaviours than boys ( Smith et al., 2008 ). The results illustrating that levels of bullying/cyberbullying and victimization/cybervictimization behaviour were similar in our two age subgroups of adolescents seem to support studies that explain how bullying/cyberbullying and victimization/cybervictimization behaviours tends to change during childhood, but stabilize during early and middle adolescence ( Tsaousis, 2016 ).

Taken together, these findings are important because they provide new evidence for an issue that previous literature has demonstrated to be either inconclusive or controversial. Although some previous studies agreed that there is a potential negative association between self-esteem and peer bullying/cyberbullying and victimization/cybervictimization behaviours, most of them were based on a variable-oriented approach. The results from the current study, however, show that among adolescents there is a self-esteem-domain heterogeneity associated with different levels of bullying/cyberbullying and victimization/cybervictimization behaviours. This suggests that different domains of self-esteem and their interdependencies play a crucial role during adolescence. Indeed, during that period the increased importance of peer relations and physical appearance may make adolescents more inclined to focus on their self-image ( Twenge & Campbell, 2001 ), with consequences also in terms of diverse patterns of aggressive behaviour.

The weaknesses of the present study should also be taken into account. First, we were not able to analyse our data according to a multilevel approach owing to the completely anonymous online administration of the questionnaire. Future research should be conducted to examine this issue. Second, we did not analyse the data in terms of the different forms of bullying/cyberbullying and victimization/cybervictimization owing to space limitations. It would be important to understand how diverse self-esteem profiles are associated with specific forms of active-passive aggressive behaviour. Finally, we only considered gender and age as variables related to bullying/cyberbullying and victimization/cybervictimization behaviours in addition to self-esteem profiles. Nevertheless, the literature suggests the need to consider other factors as well ( Bartolo et al., 2019 ; Cook, Williams, & Guerra, 2010 ).

Despite these limitations, our study presents important implications for current policies and practices, suggesting guidelines for designing educational programs aimed at preventing bullying/cyberbullying and victimization/cybervictimization phenomena. Generally, anti-bullying agencies should develop more effective individual-centred prevention programs ( Tsaousis, 2016 ). With regard to victimization/cybervictimization, it would be important to design intervention programs that help adolescents to build their own self-confidence in all important domains of their life. This implies interventions at different levels: individual, family, and school ( Tenuta, Bartolo, Diano, & Costabile, 2020 ). With regard to bullying/cyberbullying, such intervention programs should guarantee the improvement of adolescent’s self-images in at least one of the relevant areas of life, with slightly greater attention to the areas of the family and school. As a conclusive recommendation, in the case of more targeted interventions in a specific sphere, it is advisable, and even simpler, to directly involve significant people who fit within that living space, such as parents, teachers or peers (for example, in the role of mentors).

Acknowledgments

The authors have no support to report.

Biographies

Anna Lisa Palermiti is Assistant Professor in Developmental and Educational Psychology. Her main research fields are positive and negative behaviours among peers, like bullying, cyberbullying, and risk of radicalization in adolescents.

Maria Giuseppina Bartolo is postdoctoral research fellow in Developmental and Educational Psychology. Her main research fields are positive and negative behaviours among peers, like bullying, cyberbullying, and risk of radicalization in adolescents.

Pasquale Musso is Assistant Professor in Developmental and Educational Psychology. His research areas focus on social development of adolescents and emerging adults, especially as related to their positive development, acculturation processes, socio-psychological adaptation, and mutual intercultural relations.

Rocco Servidio is Assistant Professor in Social Psychology. His main research fields are cyberpsychology with a special focus on social media and Internet addiction, prejudice and intercultural relations, and risk of radicalization in adolescents.

Angela Costabile is Full Professor in Developmental and Educational Psychology. Her main research fields are primary relationships, aggressive behaviour among peers, risk of radicalization in youth. She was team leader of several national and international projects.

Funding Statement

The authors have no funding to report.

Competing Interests

The authors have declared that no competing interests exist.

• Aldenderfer, M. S., & Blashfield, R. K. (1984). Cluster analysis. Beverly Hills, CA: SAGE. [ Google Scholar ]
• Bartolo, M. G., Palermiti, A. L., Servidio, R., Musso, P., & Costabile, A. (2019). Mediating processes in the relations of parental monitoring and school climate with cyberbullying: The role of moral disengagement. Europe’s Journal of Psychology , 15 ( 3 ), 568–594. 10.5964/ejop.v15i3.1742 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Baumeister, R. F., Bushman, B. J., & Campbell, W. K. (2000). Self-esteem, narcissism, and aggression: Does violence result from low self-esteem or from threatened egotism? Current Directions in Psychological Science , 9 ( 1 ), 26–29. 10.1111/1467-8721.00053 [ CrossRef ] [ Google Scholar ]
• Beltrán-Catalán, M., Zych, I., Ortega-Ruiz, R., & Llorent, V. J. (2018). Victimisation through bullying and cyberbullying: Emotional intelligence, severity of victimisation and technology use in different types of victims. Psicothema , 30 ( 2 ), 183–188. 10.7334/psicothema2017.313 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bergman, L. R., & Trost, K. (2006). The person-oriented versus the variable-oriented approach: Are they complementary, opposites, or exploring different worlds? Merrill-Palmer Quarterly , 52 ( 3 ), 601–632. 10.1353/mpq.2006.0023 [ CrossRef ] [ Google Scholar ]
• Besag, V. E. (2006). Bullying among girls: Friends or foes? School Psychology International , 27 ( 5 ), 535–551. 10.1177/0143034306073401 [ CrossRef ] [ Google Scholar ]
• Breckenridge, J. N. (2000). Validating cluster analysis: Consistent replication and symmetry. Multivariate Behavioral Research , 35 ( 2 ), 261–285. 10.1207/S15327906MBR3502_5 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brighi, A., Guarini, A., Melotti, G., Galli, S., & Genta, M. L. (2012). Predictors of victimisation across direct bullying, indirect bullying, and cyberbullying. Emotional & Behavioural Difficulties , 17 ( 3–4 ), 375–388. 10.1080/13632752.2012.704684 [ CrossRef ] [ Google Scholar ]
• Calvete, E., Orue, I., Estévez, A., Villardón, L., & Padilla, P. (2010). Cyberbullying in adolescents: Modalities and aggressors’ profile. Computers in Human Behavior , 26 ( 5 ), 1128–1135. 10.1016/j.chb.2010.03.017 [ CrossRef ] [ Google Scholar ]
• Cénat, J. M., Hébert, M., Blais, M., Lavoie, F., Guerrier, M., & Derivois, D. (2014). Cyberbullying, psychological distress and self-esteem among youth in Quebec schools. Journal of Affective Disorders , 169 , 7–9. 10.1016/j.jad.2014.07.019 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chen, J. K., & Wei, H. K. (2011). The impact of school violence on self-esteem and depression among Taiwanese junior high school students. Social Indicators Research , 100 ( 3 ), 479–498. 10.1007/s11205-010-9625-4 [ CrossRef ] [ Google Scholar ]
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum. [ Google Scholar ]
• Cook, C. R., Williams, K. R., & Guerra, N. (2010). Predictors of bullying and victimization in childhood and adolescence: A meta-analytic investigation. School Psychology Quarterly , 25 ( 2 ), 65–83. 10.1037/a0020149 [ CrossRef ] [ Google Scholar ]
• Crick, N. R., & Grotpeter, J. K. (1995). Relational aggression, gender, and social‐psychological adjustment. Child Development , 66 ( 3 ), 710–722. 10.2307/1131945 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Donnellan, M. B., Trzesniewski, K. H., Robins, R., Moffitt, T. E., & Caspi, A. (2005). Low self-esteem is related to aggression, antisocial behavior, and delinquency. Psychological Science , 16 ( 4 ), 328–335. 10.1111/j.0956-7976.2005.01535.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• DuBois, D. L., Felner, R. D., Brand, S., & George, G. R. (1999). Profiles of self-esteem in early adolescence: Identification and investigation of adaptive correlates. American Journal of Community Psychology , 27 ( 6 ), 899–932. 10.1023/A:1022218810963 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Guo, S. (2016). A meta‐analysis of the predictors of cyberbullying perpetration and victimization. Psychology in the Schools , 53 ( 4 ), 432–453. 10.1002/pits.21914 [ CrossRef ] [ Google Scholar ]
• Harter, S. (2012). Self-perception profile for adolescents: Manual and questionnaires . Denver, CO: University of Denver Press. [ Google Scholar ]
• Harter, S., & Whitesell, N. R. (2003). Beyond the debate: Why some adolescents report stable self‐worth over time and situation, whereas others report changes in self‐worth. Journal of Personality , 71 ( 6 ), 1027–1058. 10.1111/1467-6494.7106006 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hinduja, S., & Patchin, J. W. (2007). Offline consequences of online victimization: School violence and delinquency. Journal of School Violence , 6 ( 3 ), 89–112. 10.1300/J202v06n03_06 [ CrossRef ] [ Google Scholar ]
• Jankauskiene, R., Kardelis, K., Sukys, S., & Kardeliene, L. (2008). Associations between school bullying and psychosocial factors. Social Behavior and Personality , 36 ( 2 ), 145–162. 10.2224/sbp.2008.36.2.145 [ CrossRef ] [ Google Scholar ]
• Kokkinos, C. M., & Antoniadou, N. (2019). Cyber-bullying and cyber-victimization among undergraduate student teachers through the lens of the General Aggression Model. Computers in Human Behavior , 98 , 59–68. 10.1016/j.chb.2019.04.007 [ CrossRef ] [ Google Scholar ]
• Kowalski, R. M., & Limber, S. P. (2013). Psychological, physical, and academic correlates of cyberbullying and traditional bullying. Journal of Adolescent Health , 53 ( 1 ), S13–S20. 10.1016/j.jadohealth.2012.09.018 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lei, H., Mao, W., Cheong, C. M., Wen, Y., Cui, Y., & Cai, Z. (2020). The relationship between self-esteem and cyberbullying: A meta-analysis of children and youth students. Current Psychology , 39 , 830–842. 10.1007/s12144-019-00407-6 [ CrossRef ] [ Google Scholar ]
• Li, Q. (2006). Cyberbullying in schools: A research of gender differences. School Psychology International , 27 ( 2 ), 157–170. 10.1177/0143034306064547 [ CrossRef ] [ Google Scholar ]
• Melotti, G., & Passini, S. (2002). L’applicazione ad un gruppo di adolescenti italiani del Self-Esteem Questionnaire (SEQ) [The application of the Self-Esteem Questionnaire (SEQ) to a group of Italian adolescents]. Bollettino di Psicologia Applicata , 238 , 59–70. [ Google Scholar ]
• Moreira, P. A., Inman, R. A., Cloninger, K., & Cloninger, C. R. (2021). Student engagement with school and personality: A biopsychosocial and person‐centred approach. British Journal of Educational Psychology , 91 ( 2 ), 691–713. 10.1111/bjep.12388 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Morin, A. J., Bujacz, A., & Gagné, M. (2018). Person-centered methodologies in the organizational sciences: Introduction to the feature topic. Organizational Research Methods , 21 ( 4 ), 803–813. 10.1177/1094428118773856 [ CrossRef ] [ Google Scholar ]
• Moses, M., & Williford, A. (2017). Individual indicators of self-reported victimization among elementary school-age students: A latent class analysis. Children and Youth Services Review , 83 , 33–40. 10.1016/j.childyouth.2017.10.006 [ CrossRef ] [ Google Scholar ]
• Mroczek, D. K., & Little, T. D. (2014). Handbook of personality development . New York: Psychology Press. [ Google Scholar ]
• Olweus, D. (1993). Bullying at school: What we know and what we can do . Malden, MA: Blackwell Publishing. [ Google Scholar ]
• Palermiti, A. L., Servidio, R., Bartolo, M. G., & Costabile, A. (2017). Cyberbullying and self-esteem: An Italian study. Computers in Human Behavior , 69 , 136–141. 10.1016/j.chb.2016.12.026 [ CrossRef ] [ Google Scholar ]
• Palladino, B. E., Nocentini, A., & Menesini, E. (2016). Evidence-based intervention against bullying and cyberbullying: Evaluation of the NoTrap! program in two independent trials. Aggressive Behavior , 42 ( 2 ), 194–206. 10.1002/ab.21636 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Patchin, J. W., & Hinduja, S. (2010). Cyberbullying and self-esteem. Journal of School Health , 80 ( 12 ), 614–621. 10.1111/j.1746-1561.2010.00548.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pearce, J. B., & Thompson, A. E. (1998). Practical approaches to reduce the impact of bullying. Archives of Disease in Childhood , 79 ( 6 ), 528–531. 10.1136/adc.79.6.528 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ricciardelli, L. A., & McCabe, M. P. (2001). Self-esteem and negative affect as moderators of sociocultural influences on body dissatisfaction, strategies to decrease weight, and strategies to increase muscles among adolescent boys and girls. Sex Roles , 44 ( 3/4 ), 189–207. 10.1023/A:1010955120359 [ CrossRef ] [ Google Scholar ]
• Rodríguez-Enríquez, M., Bennasar-Veny, M., Leiva, A., Garaigordobil, M., & Yañez, A. M. (2019). Cybervictimization among secondary students: Social networking time, personality traits and parental education. BMC Public Health , 19 e1499. 10.1186/s12889-019-7876-9 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Salmivalli, C., Kaukiainen, A., Kaistaniemi, L., & Lagerspetz, J. M. K. (1999). Self-evaluated self-esteem, peer-evaluated self-esteem, and defensive egotism as predictors of adolescents’ participation in bullying situations. Personality and Social Psychology Bulletin , 25 ( 10 ), 1268–1278. 10.1177/0146167299258008 [ CrossRef ] [ Google Scholar ]
• Salmivalli, C., Lagerspetz, K., Björkqvist, K., Österman, K., & Kaukiainen, A. (1996). Bullying as a group process: Participant roles and their relations to social status within the group. Aggressive Behavior , 22 ( 1 ), 1–15. 10.1002/(SICI)1098-2337(1996)22:1<1::AID-AB1>3.0.CO;2-T [ CrossRef ] [ Google Scholar ]
• Smith, P. K. (2014). Understanding school bullying: Its nature and prevention strategies . London: SAGE. [ Google Scholar ]
• Smith, P. K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S., & Tippett, N. (2008). Cyberbullying: Its nature and impact in secondary school pupils. Journal of Child Psychology and Psychiatry, and Allied Disciplines , 49 ( 4 ), 376–385. 10.1111/j.1469-7610.2007.01846.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Smith, P. K., & Slonje, R. (2010). Cyberbullying: The nature and extent of a new kind of bullying, in and out of school. In S. Jimerson; S. Swearer, & D. Espelage (Eds.), The international handbook of school bullying (pp. 249–262). New York: Routledge. [ Google Scholar ]
• Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics , (6th ed.). Boston, MA: Allyn & Bacon. [ Google Scholar ]
• Templeton, G. F. (2011). A two-step approach for transforming continuous variables to normal: Implications and recommendations for IS research. Communications of the Association for Information Systems , 28 e4. 10.17705/1CAIS.02804 [ CrossRef ] [ Google Scholar ]
• Tenuta, F., Bartolo, M. G., Diano, D., & Costabile, A. (2020). Maltreatment and abuse: A review of definitions, types, and interventions for children at risk. Maltrattamento e Abuso all'Infanzia , 2020 ( 2 ), 85–106. 10.3280/MAL2020-002005 [ CrossRef ] [ Google Scholar ]
• Tsaousis, I. (2016). The relationship of self-esteem to bullying perpetration and peer victimization among schoolchildren and adolescents: A meta-analytic review. Aggression and Violent Behavior , 31 , 186–199. 10.1016/j.avb.2016.09.005 [ CrossRef ] [ Google Scholar ]
• Twenge, J. M., & Campbell, W. K. (2001). Age and birth cohort differences in self-esteem: A cross-temporal meta-analysis. Personality and Social Psychology Review , 5 ( 4 ), 321–344. 10.1207/S15327957PSPR0504_3 [ CrossRef ] [ Google Scholar ]
• von Eye, A., & Bergman, L. R. (2003). Research strategies in developmental psychopathology: Dimensional identity and the person-oriented approach. Development and Psychopathology , 15 ( 3 ), 553–580. 10.1017/S0954579403000294 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• von Eye, A., & Bogat, G. A. (2006). Person-oriented and variable-oriented research: Concepts, results, and development. Merrill-Palmer Quarterly , 52 ( 3 ), 390–420. 10.1353/mpq.2006.0032 [ CrossRef ] [ Google Scholar ]
• von Soest, T., Wichstrøm, L., & Kvalem, I. L. (2016). The development of global and domain-specific self-esteem from age 13 to 31. Journal of Personality and Social Psychology , 110 ( 4 ), 592–608. 10.1037/pspp0000060 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wichstrøm, L., & von Soest, T. (2016). Reciprocal relations between body satisfaction and self-esteem: A large 13-year prospective study of adolescents. Journal of Adolescence , 47 ( 1 ), 16–27. 10.1016/j.adolescence.2015.12.003 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Williams, K. R., & Guerra, N. G. (2007). Prevalence and predictors of internet bullying. Journal of Adolescent Health , 41 ( 6 ), S14–S21. 10.1016/j.jadohealth.2007.08.018 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ybrandt, H., & Armelius, K. (2010). Peer aggression and mental health problems: Self-esteem as a mediator. School Psychology International , 31 ( 2 ), 146–163. 10.1177/0143034309352267 [ CrossRef ] [ Google Scholar ]