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Umbrella review of meta-analyses on the risk factors, protective factors, consequences and interventions of cyberbullying victimization | Nature Human Behaviour
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Umbrella review of meta-analyses on the risk factors, protective factors, consequences and interventions of cyberbullying victimization | Nature Human Behaviour

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Nov 20, 2024 01:26 PM
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https://www.nature.com/articles/s41562-024-02011-6
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Abstract

The increasing prevalence of cyberbullying victimization has become a commonplace issue globally. Although research has explored various predictors and consequences of cyberbullying victimization, most focus on a narrow range of variables or contexts, highlighting the need to comprehensively review and synthesize the wealth of empirical findings. We conducted a systematic review of meta-analyses on cyberbullying victimization, incorporating 56 meta-analyses and 296 effect sizes (sample size range 421–1,136,080, sample size median 53,183; searched via EBSCOhost ERIC, EBSCOhost PsycInfo, PubMed, Scopus, Web of Science, 13 cyberbullying-related journals, Google Scholar and ProQuest Dissertations and Theses) to address the following critical questions: (1) What are the crucial sociodemographic and psychological profiles of cyberbullying victims? (2) What critical contextual and environmental factors are associated with cyberbullying victimization? (3) What are the key psychological and behavioural consequences of cyberbullying victimization? (4) How effective are existing interventions in mitigating impacts of cyberbullying? Included meta-analyses had to focus on cyberbullying victimization and report at least one predictor or consequence. A quality assessment was conducted using the Joanna Briggs Institute Critical Appraisal Instrument for Systematic Reviews and Research Syntheses. Findings suggest that females, school-aged populations, traditional bullying victims and frequent internet users were more likely to be cyberbullied. Unregulated school environments and unsupportive parental relationships were also associated with increased cyberbullying victimization. Cyberbullying victimization was consistently associated with negative psychological outcomes, lower school performance and maladaptive coping behaviours. More importantly, the current review found that cyberbullying intervention programmes show promising results. The current review underscores the importance of devoting adequate resources to mitigating cyberbullying victimization.

Main

Amid rapid technological advancements, the internet has become a prevalent platform for social interaction, particularly among youth and adolescents123. These digital environments, while fostering connections and personal expression45678, present new challenges9, including cyberbullying—an important and growing concern10. Referring to intentional acts of aggression carried out via electronic media11, cyberbullying has become a commonplace issue in recent times1213. Globally, around four in ten adults who use the internet have experienced cyberbullying14. In the United States, nearly half of adolescents have experienced at least one instance of cyberbullying15. Within Asia, countries such as Singapore, China, Malaysia and South Korea all report high prevalence rates close to 50% (refs. 1617).
This increasing prevalence of cyberbullying with greater digital media use, however, does not uniformly indicate that more internet usage directly leads to more instances of cyberbullying1819. Indeed, the relationship between increased digital activity and cyberbullying is influenced by various factors, such as digital literacy2021, the availability of social support networks222324 and the effectiveness of preventative measures2526. These factors vary widely across different social and cultural contexts, highlighting the complexity of cyberbullying as a phenomenon. Given this complexity, defining cyberbullying precisely is essential for the effective dissection of these contributing factors.

Defining cyberbullying

While there is currently no consensus within research on the precise definition of cyberbullying2728, there are some universally accepted elements. First, it is widely recognized that cyberbullying involves electronic media28. The term ‘electronic media’ itself is broad; some definitions restrict it to internet and mobile phones29303132, while others apply a more detailed taxonomy of technology3334. Given the rapid evolution of technology, it is pragmatic to adopt a broader definition that encompasses both current and forthcoming technologies by using a definition such as ‘actions carried out via any electronic means’ rather than specifying devices through which cyberbullying occur.
Second, it is also generally agreed that cyberbullying involves a form of aggression towards an individual or a group2835. However, different studies operationalize aggression differently. For instance, most research identifies cyberbullying through behaviours such as sending aggressive messages online28353637, whereas Mills et al.38 operationalized cyberbullying as online social exclusion. Willard39 developed a comprehensive taxonomy of cyberbullying that includes flaming, online harassment, outing and trickery, sexting, exclusion, impersonation and cyberstalking.
In consideration, the current work defined cyberbullying as any aggressive or bullying behaviour aimed towards an individual or a group using any electronic means. This definition encompasses aspects such as sextortion (threatening to use an explicit photo or video of someone to make demands/pressure them40), online social exclusion (excluding an individual via blocking or distancing over online means39) and cyberdating abuse (a form of control and harassment by the dating partner using electronic media41), as these behaviours involve aggressive acts via electronic media.

Cyberbullying versus traditional bullying

It is widely accepted that cyberbullying is an extension of traditional bullying42, with many researchers modelling their definitions of cyberbullying on the main characteristics of traditionally bullying43: intention, repetition and power imbalance44. While there is a high correlation between traditional bullying and cyberbullying284546, marked differences exist between the two. First, the intention behind cyberbullying can often be ambiguous to the victim owing to the lack of non-verbal cues; actions perceived as humorous by the perpetrator might be interpreted as hurtful by the recipient2747. Second, the concept of repetition differs within the online realm; perpetrators may only commit a single aggressive act to victims, but that one post, comment, or image does not need to be reposted by the original perpetrator to be considered repetitive4849. It can be shared or forwarded by others, continually harming the victim without further direct action from the perpetrator. Third, power imbalances are not always a prerequisite for cyberbullying42. Owing to the anonymity of digital platforms and the lack of physical confrontation, individuals who may not typically engage in face-to-face bullying can easily perpetrate online harassment47. While research on cyberbullying has attempted to define power imbalance in terms of digital literacy50, this may not necessarily confer notable advantages in the current environment as the proliferation of various platforms and their ease of use has simplified the act of bullying online27.
Most crucially, by eliminating the need for face-to-face interaction and allowing anonymity5152, cyberbullying allows for online disinhibition53. According to the Online Disinhibition Effect Thoery53, the internet, which offers anonymity by allowing users to adopt usernames, allows individuals to separate their online actions from their offline identity. This reduces the sense of responsibility for their online actions and motivates perpetrators to engage in cyberbullying, which increases the possible incidence of victimization53 and allows victims themselves to become future cyberbullies5455. Thus, it is imperative to better understand cyberbullying as a phenomenon distinct from traditional bullying to prevent creating a vicious cycle of internet-based aggressive behaviour that perpetuates negative consequences.

Measurement issues in cyberbullying research

Another focal point within cyberbullying research is the challenge of measurement. The absence of a unified definition complicates measurement as studies often adopt divergent definitions and employ various scales that may not fully capture the phenomenon. For instance, some studies limit cyberbullying to online peer victimization3456, while others do not2933. Additionally, older studies frequently omit definitions of cyberbullying2857. While newer studies tend to provide one, they vary substantially in word choice, using terms such as ‘cyberaggression’, ‘cyberstalking’ or ‘cyberbullying’, which can confuse respondents and hinder comparability across studies58. The development of cyberbullying scales also shows inconsistencies, with many not adhering to recommended guidelines for item development and only about half reporting validity statistics58. Moreover, the rapid evolution of digital platforms continually outdates older cyberbullying scales that may not account for newer methods of cyberbullying59.
These measurement challenges are intensified by the need to consider developmental stages. Children, adolescents and adults can experience and interpret cyberbullying in fundamentally different ways due to their developmental cognitive and social capacities60. For example, younger children may lack emotional maturity to accurately identify cyberbullying incidents61, whereas adolescents, as they become more integrated with society, may both experience it more and also be able to identify it60. Adults, on the other hand, might interpret interactions differently based on life experiences and maturity, influencing their responses to potential cyberbullying scenarios1362. This variability across age groups necessitates the synthesis of unique and common factors of cyberbullying to develop more robust cyberbullying measures and identify universally applicable predictors and consequences.
These complex issues within defining and measuring cyberbullying, combined with its potentially severe effects on victims, emphasize the importance of holistically synthesizing existing research. Thus, it is essential to better understand four major areas of research within cyberbullying: (1) sociodemographic and psychological profiles of victims, (2) various contextual and environmental predictors of cyberbullying victimization, (3) the consequences of cyberbullying victimization and (4) the efficacy of existing intervention programmes aimed at preventing cyberbullying.

Sociodemographic and psychological predictors

One primary question in cyberbullying research revolves around identifying sociodemographic and psychological profiles of cyberbullying victims. In terms of sociodemographic factors, research has shown that females and minorities (that is, racial and sexual) were more likely to be subjected to cyberbullying victimization63646566. Furthermore, personality traits, such as neuroticism and low agreeableness, can contribute to cyberbullying by affecting how individuals interact online and perceive hostile interactions67. Individuals with higher levels of anxiety, depression and anger are also more likely to become victims of cyberbullying285568, as they tend to be distanced from social groups and resort more to online media6970.

Contextual and environmental predictors

Another important research question discussed within cyberbullying research concerns the contextual and environmental factors associated with cyberbullying victimization. Unregulated family and school climates, as well as unrestricted internet use, were prominent contextual risk factors associated with cyberbullying victimization64717273. Unregulated environments provide vulnerable targets and allow the unrestrained perpetration of cyberbullying in the absence of parental guardians or teachers, consistent with the Routine Activity Theory—deviant behaviours such as cyberbullying occur in the presence of motivated offenders, suitable targets and an absence of capable guardians747576. The Routine Activity Theory suggests that the lack of effective supervision increases the opportunity for cyberbullying, emphasizing the importance of considering environmental factors as a predictor of cyberbullying victimization.

Psychological and behavioural consequences

Third, another essential question within cyberbullying literature pertains to understanding the consequences of online victimization4677787980. Mental health problems such as depression, anxiety and suicidal ideation are commonly identified as psychological consequences of cyberbullying victimization36818283. Research indicates that this relationship between psychological problems and cyberbullying victimization is bidirectional, as individuals with pre-existing conditions are more vulnerable to cyberbullying, which in turn exacerbates their symptoms84. Furthermore, these psychological consequences can snowball into behavioural consequences as well. Cyberbullying victims show lower school attendance, academic achievement2885 and worse peer relationships82 and tend to engage more in both traditional and cyberbullying perpetration3686. This is in line with the General Strain Theory, as the negative emotional strain caused by being cyberbullied may lead individuals to engage in deviant acts such as bullying, especially in the anonymized cyberspace188788.

Effectiveness of interventions

Another key question frequently explored in cyberbullying research concerns the effectiveness of interventions specifically designed to prevent cyberbullying. Presently, many intervention programmes focus on educating individuals about cyberbullying and equipping them with coping strategies to handle its risk factors8990. Additionally, some studies highlight various programme types that incorporate digital interventions89 and emphasize the involvement of specific social groups, such as families91. However, the effectiveness of these anti-cyberbullying programmes remains uncertain, as indicated by previous reviews that report mixed results2692.

The current review

Despite the extensive investigations into factors linked with cyberbullying victimization and the consolidation of predictors and outcomes through meta-analytic studies, there is a lack of comprehensive synthesis of these meta-analyses. While many predictors and consequences are associated with cybervictimization, most of the existing meta-analyses focus on assessing a single factor’s relationship with cyberbullying9394. For example, Barlett and Coyne95 solely examined age as a risk factor associated with cybervictimization, while Sun and Fan66 solely focused on the association between gender and cyberbullying victimization. Considering that being a victim of cyberbullying is usually the result of a combination of risk factors rather than one individual factor and that cyberbullying can lead to a diverse range of effects80, it is pertinent to combine the various meta-analyses and gain a holistic understanding of the interconnectedness between the risks and outcomes of cyberbullying victimization.
Thus, the current work aims to conduct a systematic review of meta-analyses on potential predictors and consequences associated with cyberbullying victimization. Using a systematic review methodology will offer the opportunity to examine a broad scope of factors investigated by scholars and consider whether there is consensus in the field9697. Specifically, this review will address the following critical questions: (1) What are the crucial sociodemographic and psychological profiles of cyberbullying victims? (2) What critical contextual and environmental factors are associated with cyberbullying victimization? (3) What are the key psychological and behavioural consequences of cyberbullying for victims? (4) How effective are existing interventions in mitigating the impacts of cyberbullying? By summarizing the associations reported in meta-analyses, this review aims to provide a clearer picture regarding the phenomena of cyberbullying victimization.

Results

Search outcome and eligibility

As illustrated in the Preferred Reporting Items for Systematic Reviews and Meta-Analyse (PRISMA) flowchart (Fig. 8), the initial search returned 1,583 records, of which 1,149 remained after the removal of duplicates. Title and abstract screening resulted in the removal of a further 818 records. Full text-screening resulted in the removal of 331 records, leaving a final total of 56 records1625262835363738646566677382858689909193949899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132, covering all regions (see Fig. 6 for full details). The characteristics of the 56 included meta-analyses are presented in Table 1 (see Fig. 1 for more descriptive statistics).
Table 1 Characteristics of the 56 included meta-analyses
notion image
An assessment using criteria according to the JBI Critical Appraisal of Systematic Reviews and Research Synthesis.
a, The years in which the included meta-analyses were published or made available. b, The span of years of the studies included within the meta-analyses. c, A representation of the sample sizes across the included meta-analyses. The data were derived from the 56 meta-analyses included in the review (n = 56). The violin plot displays the density distribution of the data, while the overlaid box plot shows the median (54,314), interquartile range (98,724.75) and full range of the data (421–1,136,080). The black dots represent outliers, indicating sample sizes that deviate noticeably from the rest of the data distribution. d, The spread of the number of studies within the included meta-analyses. The data were derived from 56 meta-analyses included in the review (n = 56). The violin plot displays the density distribution of the data, while the overlaid box plot shows the median (28.5), interquartile range (38.25) and full range of the data (2–212). The black dots represent outliers, indicating numbers of studies that deviate noticeably from the rest of the data distribution. e, The included meta-analyses by the type of publication. f, The age groups of the samples included within the meta-analyses.

Quality of included records

Based on the Joanna Briggs Institute (JBI) Critical Appraisal Instrument for Systematic Reviews and Research Syntheses tool, methodological quality scores for included records ranged from 6 to 11 (median 9; see Table 2 for a breakdown of the quality appraisal scores by record and Fig. 2 for a breakdown of the quality appraisal scores by criteria). As all 56 records had at least six ‘yes’ responses, it was concluded that there was no discernible methodological bias within any of the included meta-analyses. Of note, 53.57% (n = 30) of the included meta-analyses did not conduct quality appraisal of their constituent empirical studies, and 46.43% of the included meta-analyses (n = 26) did not use an adequate breadth of sources within their search strategy (for example, did not search for unpublished literature).
An assessment using criteria according to the JBI Critical Appraisal of Systematic Reviews and Research Synthesis.
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Table 2 Methodological quality assessment of the included meta-analyses by record according to the JBI Critical Appraisal of Systematic Reviews and Research Synthesis

Overall results

Predictors of cyberbullying victimization

In total, 39 out of the 56 included records included effect sizes on the relationship between cyberbullying victimization and its predictors.

Sociodemographic and personality predictors

A total of 13 meta-analyses explored sociodemographic and personality factors associated with cyberbullying victimization (Fig. 3a). Within them, ten meta-analyses examined sociodemographic factors, including age, gender, minority status and socioeconomic background. Six out of the seven meta-analyses focusing on age—all of which focused on children, adolescents or college-aged samples—indicated that age denoted a higher risk of becoming a cyberbullying victim (median r = 0.07, range −0.02 to 0.40). Furthermore, 8 out of 11 meta-analyses indicated that females were more likely than males to be victims of cyberbullying victimization (median r = 0.04, range −0.14 to 0.27). With regard to marital status, no significant effect was observed across the records (median r = −0.01, range −0.09 to 0.08). Across four of the five meta-analyses that examined minority status, members of both racial/ethnic and sexual minorities were more likely to become cyberbullying victims as compared with majority groups (Caucasians and heterosexuals, respectively) (median r = 0.03, range −0.03 to 0.20). Finally, two meta-analyses indicated that indicators of higher socioeconomic status (including parental education) were associated with higher exposure to cyberbullying victimization.
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a
, The association between sociodemographic and personality predictors and cyberbullying victimization based on each individual review. b, The association between psychological predictors and cyberbullying victimization based on each individual review. For a and b, r and the 95% confidence interval (CI) refer to the correlation between cyberbullying victimization and the predictor of interest. n refers to the sample size corresponding to each row (‘not stated’ is used in cases where meta-analyses did not provide relevant information), and k refers to the number of effect sizes used to calculate the correlation in each row.
Positively valenced personality traits—such as agreeableness, extraversion and openness to experience—were associated with lower cyberbullying victimization (median r = −0.09, range −0.18 to −0.06), while negatively valenced personality traits—such as antisocial personality, dark personality traits, dominance and neuroticism—were associated with increased risk of exposure to cyberbullying victimization (median r = 0.15, range −0.06 to 0.23).

Psychological predictors

A total of 14 meta-analyses explored psychological factors predicting cyberbullying victimization (Fig. 3b). Across all meta-analyses, higher levels of mental health risk factors and behavioural problems were both associated with increased levels of cyberbullying victimization. Internalizing mental health problems associated with cyberbullying victimization included higher levels of anxiety, higher levels of depression, higher levels of moral disengagement and various psychiatric conditions, all of which were related to an increased tendency to be a victim of cyberbullying (median r = 0.15, range 0.07 to 0.38). All meta-analyses also indicated that high levels of externalizing problems, including anger and hostility, behavioural problems (including risky behaviours) and substance use, were positively related to cyberbullying victimization (median r = 0.16, range −0.01 to 0.57). In contrast, 16 out of 18 effect sizes indicated that positively valenced psychological factors such as emotional intelligence, better emotional management, empathy, higher self-control, higher self-efficacy, higher self-esteem and higher social intelligence served as protective factors against cyberbullying victimization (median r = −0.06, range −0.22 to 0.12).

Contextual predictors

A total of 29 meta-analyses reported various contextual predictors of cyberbullying victimization (Fig. 4a). Within them, 12 meta-analyses included parental and family relations as a contextual predictor. Overall, 13 out of 14 effect sizes indicated that a positive family environment was associated with lower levels of cyberbullying victimization. Higher levels of family support and parental monitoring, including parental control of technology, parental interaction, parental mediation and parental support, were also associated with lower risk of being subjected to cyberbullying victimization (median r = −0.08, range −0.18 to 0.01). In contrast, results from three meta-analyses indicated that unfavourable home environments, such as experiencing childhood maltreatment, offensive family communication or being part of single-parent households, were associated with increased exposure to cyberbullying victimization (median r = 0.20, range 0.16 to 0.24). Furthermore, three meta-analyses showed that being in an intimate relationship and characteristics of the relationship (including high violence perpetration and or/victimization within the relationship) were also associated with higher levels of cyberbullying victimization (median r = 0.14, range −0.05 to 0.44).
notion image
a
, The association between parental and family relations and cyberbullying victimization based on each individual review. b, The association between school-related, peer-related and environmental factors and cyberbullying victimization based on each individual review.
The association between school-related, peer-related and environmental factors and cyberbullying victimization was included in 11 meta-analyses (Fig. 4b). Five effect sizes indicated that negative school climates and lack of school safety were associated with higher cyberbullying victimization (median r = 0.11, range 0.01 to 0.22). Similarly, lower peer relationship quality, negative peer influence and being the perpetrator or victim of traditional peer bullying were associated with a higher cyberbullying victimization across all meta-analyses (median r = 0.25, range 0.09 to 0.49).
Factors related to internet use were also common contextual predictors of cyberbullying victimization, as seen in nine meta-analyses (Fig. 5a). They included higher frequency and type of internet use, internet addiction, risky online behaviour and being perpetrators and victims of cyberbullying previously, all of which were associated with increased cyberbullying victimization in 19 out of 23 effect sizes (median r = 0.19, range −0.11 to 0.87).
notion image
a
, The association between factors related to internet use and cyberbullying victimization based on each individual review. b, The association between participating in anti-cyberbullying programmes and cyberbullying victimization based on each individual review. IT, information technology.
Finally, taking part in anti-cyberbullying interventions, including both school-based programmes and parental education programmes, was indicated by 11 meta-analyses as consistently associated with lower levels of cyberbullying victimization (median r = −0.07, range −0.14 to −0.04) (Fig. 5b).

Consequences of cyberbullying victimization

In total, 25 out of the 56 included records included effect sizes on the relationship between cyberbullying victimization and its consequences.

Psychological consequences

A total of 21 meta-analyses provided effect sizes regarding associations between cyberbullying victimization and psychological consequences (Fig. 6). Overall, internalizing and emotional problems were common consequences of cyberbullying victimization. Individuals experiencing cyberbullying victimization displayed increased anxiety, depression, emotional problems, stress, loneliness and moral disengagement in 30 out of 31 effect sizes (median r = 0.24, range −0.04 to 0.35). Furthermore, victims of cyberbullying were also more likely to show tendencies of self-harm and suicidal behaviour in all examined meta-analyses (median r = 0.29, range 0.04 to 0.40). Conversely, cyberbullying victimization was negatively associated with positively valenced psychological variables in all meta-analyses (median r = −0.150, range −0.310 to −0.003), with victims showing decreased levels of empathy, life satisfaction and self-esteem.
The association between cyberbullying victimization and psychological predictors based on each individual review.
notion image

Behavioural consequences

Nine meta-analyses provided associations between cyberbullying victimization and behavioural predictors (Fig. 7). In 17 out of 18 effect sizes, higher cyberbullying victimization was associated with higher levels of externalizing behaviours and behavioural problems (median r = 0.22, range −0.26 to 0.61), including aggressive behaviour and traditional bullying perpetration, cyberbullying perpetration, conduct problems, increased social problems, less prosocial behaviour, risky sexual behaviour and increased drug and alcohol use.
The association between cyberbullying victimization and behavioural predictors based on each individual review.
notion image
Four meta-analyses also indicated associations between higher levels of cyberbullying victimization and school-related outcomes (median r = 0.14, range 0.06 to 0.36). Being subjected to increased levels of cybervictimization was associated with decreased levels of academic achievement, lower school attendance and worse peer relationships, as well as being subjected to both traditional and cyberbullying in the long term.

Discussion

The current systematic review examines meta-analyses on the predictors and consequences associated with cyberbullying victimization. A total of 56 meta-analyses, with a total of 296 effect sizes, were reviewed within the current work. The umbrella review approach made it possible to consider a broad scope of factors investigated by scholars and consider whether consensus in the field has been met on the factors that cause cyberbullying victimization and its consequences9697. Our findings begin with a detailed analysis of the sociodemographic predictors, revealing nuanced differences in vulnerability among various groups. The subsequent sections delve into the psychological and contextual factors, each highlighted by distinct patterns and relationships that emerge from the meta-analytical data. The central findings derived from the analysis provide a holistic view of the potential predictors and consequences of cyberbullying victimization and serve as a basis for future research as well as interventions. We now discuss the ten central findings of the current review.

Females are more likely to be subjected to cyberbullying victimization but are over-represented in cyberbullying research

Meta-analyses consistently show that females (versus males) are at a slightly higher risk of cyberbullying victimization37646566103107117. Females engage more with cyberbullying as both perpetrators and victims owing to their higher involvement in indirect forms of aggression133134 and more frequent use of social networking sites135136. Their tendency to share more personal information online increases their vulnerability137138. Furthermore, females may interpret online comments as hurtful more quickly than males139, contributing to higher reported levels of victimization and may, therefore, be overrepresented in cyberbullying research140141.

Age appears to have a curvilinear relationship with cyberbullying victimization

Existing findings indicate a nonlinear relationship between age and cyberbullying victimization, with victimization rates increasing with age283767117123 but only until adulthood64. Research shows that as children and adolescents age, their increased use of computers, integration into social media and exposure to digital devices heighten their cyberbullying risk140. However, victimization rates flatten in older adults, potentially owing to cyberbullying’s lower prevalence in this group and a general decrease in aggressive behaviours with age134862. These trends suggest that the impact of age on cyberbullying must be cautiously interpreted, recognizing that, while youth are increasingly vulnerable, older adults may be less affected.

Cyberbullying victims are likely to become cyberbullying perpetrators in future

Cyberbullying victimization was associated with future perpetration across three meta-analyses678299127. Unlike traditional bullying, which often involves physical disparities142, cyberbullying occurs online, enabling victims to become bullies more easily due to the absence of physical disadvantage143. Online anonymity complicates identifying bullies and victims, allowing victims to adopt the role of bullies as a form of retaliation51144. This ability to switch roles contributes to a vicious cycle of cyberbullying, escalating its negative impact within the online sphere.

Cyberbullying victimization is associated with negative psychological outcomes and lower school performance, which may lead to maladaptive behaviours

Four meta-analyses consistently show that cyberbullying victims often display lower school performance28828599, leading to considerable psychological distress and maladaptive coping behaviours28368294130. Hurtful online comments can make victims feel isolated and emotionally distressed, resulting in feelings of hopelessness, lowered self-esteem and increased anxiety, which often culminate in depression69145. These emotional burdens can reduce attendance and participation in school and social activities85, further impacting academic performance. Consequently, victims may engage in deviant behaviours such as aggression, substance use and risky sexual behaviour283682116 as coping mechanisms to offset psychological and academic issues146. This cycle of adverse effects is supported by the developmental cascades model147, which links the snowball effect of stressors, such as cyberbullying, to escalating externalizing behaviour148.

Negative psychological consequences of cyberbullying victimization increase the possibility of future victimization

Fourteen meta-analyses reveal that cyberbullying victimization was associated with considerable psychological impacts, including anxiety, depression, low empathy, reduced life satisfaction, loneliness, low self-esteem and stress, serving both as outcomes and predictors28368293949899101104112113119124130. Victims often experience isolation and negative emotions69, leading to hopelessness and depression145, which are, in turn, linked to self-harm and suicidal ideation149. Further, emotional vulnerabilities, such as poor anger management, antisocial tendencies or externalizing behaviours, can increase the likelihood of becoming a cyberbullying victim150. Research by Guo37 supports that higher aggressive cognition predicts increased cyberbullying victimization. Victims, often distanced from social groups owing to such antisocial or aggressive tendencies, appear more susceptible to bullies and are prone to seek interactions through online media6970, increasing their risk of encountering perpetrators151. This dynamic underscores the cyclical nature of cyberbullying, where the psychological effects also become risk factors, perpetuating victim vulnerability.

Parental support is a consistent protective factor against cyberbullying victimization, but the effect tends to be small

Nine meta-analyses indicate a small positive correlation between strong family relationships and a reduced risk of cyberbullying victimization283764677399103104123. Children and adolescents with involved parents, who monitor their internet use and are informed about their online experiences, are less likely to be victimized28152. This parental mediation acts as a protective factor, aligning with the Routine Activity Theory, which emphasizes the role of capable guardians as a protective factor against experiencing deviant acts747576153. However, the effect remains limited as children’s cyber activities often extend beyond parental supervision, especially in settings such as schools154.

Individuals in non-supportive romantic relationships are at higher risk of cyberbullying victimization

Results from three meta-analyses indicate that negative relationships with intimate partners significantly increase the risk of cyberbullying victimization to a small extent6467107. This heightened risk often stems from the fact that the perpetrator of cyberbullying is frequently the same individual involved in negative in-person interactions, especially in cases of cyber-dating harassment155156. Interestingly, Wissink et al.67 observed an association, albeit non-significant, of having younger partners being linked to higher cyberbullying victimization. This is possibly because younger couples, being more active online, may encounter cyberbullying more frequently157. This observation is noteworthy, as it highlights potential age-related dynamics in cyberbullying within intimate relationships.

Lack of teacher–student interactions in school are associated with higher levels of cyberbullying victimization

Ten meta-analyses reveal that unfavourable school climates lacking proper teacher–student interactions have been consistently associated with small increases in cyberbullying victimization283767738299104108118132. These environments, which also foster traditional bullying due to minimal supervision, allow unrestricted access to digital media and school devices, exacerbating cyberbullying risks158159160. According to the Routine Activity Theory, such settings enable cyberbullies to operate unimpeded and leave victims vulnerable without teacher support747576. Additionally, traditional bullying victimization and perpetration are both significantly associated with increased cyberbullying victimization2873, and as negative school climates facilitate traditional bullying, it can indirectly have a further heightening effect on the risk of cyberbullying victimization.

Active internet users are more likely to become cyberbullying victims, especially when they engage in risky online behaviour

A small but significant association was observed between increased internet and digital media use and higher cyberbullying victimization rates across seven meta-analyses3767738299104127. Active internet users are more more likely to encounter cyberbullying perpetrators161162, particularly when engaging in risky behaviours such as revealing private details online or visiting unverified websites. For example, sharing personal photos or details online increases vulnerability to attacks163164, and visiting new websites without verifying their safety can expose personal information, attracting cyberbullying165.

Anti-cyberbullying intervention programmes are effective in reducing cyberbullying

Participation in cyberbullying intervention programmes has been consistently shown across 11 meta-analyses to have a small but significant effect in reducing victimization2526899091102105122125128. These findings were consistent, regardless of whether the programme was school based, targeting children and adolescents168990105 or home based, aimed at increasing parental awareness91. These interventions typically focus on educating about cyberbullying, identifying and mitigating risky behaviours and, sometimes, include a component for parental training26166167168. By addressing such risk and protective factors, these programmes effectively reduce the likelihood of cyberbullying victimization.

Limitations

The current review has several limitations. First, although this review provides an overview of a wide range of findings, it is unable to study the finer details included in either the meta-analyses or the original primary studies. While the umbrella review approach allows studying aggregated findings to reveal more precise and generalizable results that could not be arrived at via analysing single empirical studies169, it does not facilitate the studying of more detailed aspects of various studies (for example, different moderators, types of measure utilized and response time frames). As such, it is important to consider these nuances by directing attention to the individual meta-analyses contained in the current review, as well as the various studies cited within them. Second, the current review only considered associations between cyberbullying victimization and various predictors and consequences in the form of correlations. As a majority of the included meta-analyses did not report directional or otherwise lagged findings, it was not possible to consider the directional relationship between factors within the scope of the current review.

Research gaps and potential future research

The current review highlights several research gaps in cyberbullying victimization literature. First, most meta-analyses focus on child or adolescent populations. Given that cyberbullying victimization differs based on age group1362, and older adults may have different reactions to cyberbullying victimization than younger populations, research involving broader demographics is needed to better understand its impact on different age groups.
Second, the meta-analyses included in this review mainly defined cyberbullying by focusing on the different media platforms through which cyberbullying occurs, rather than on the different acts of cyberbullying2882104119. However, research suggests that individuals do not distinguish cyberbullying based on the medium used but rather on the nature of the bullying acts themselves170. Therefore, future work should aim to refine definitions that emphasize behaviours involved in cyberbullying and incorporate behavioural measurements within cyberbullying scales to more accurately capture the phenomenon.
Third, there was a lack of meta-analyses on cyberbullying related to intimate-partner relations. A majority of the included meta-analyses focused on peer-cyberbullying victimization within young samples and were, therefore, unable to examine intimate partner relations. However, as the current review reveals that intimate partner relations can have considerable impacts on an individual’s tendency to become a cyberbullying victim, it is important for future research to consider cyber-related intimate partner violence as a branch of cyberbullying and explore further into its risk factors.
Lastly, while interventions were identified as a protective against cyberbullying victimization, the meta-analyses lacked long-term follow-up data. As analysing long-term impacts of anti-cyberbullying interventions is important to better understand the impact of such programmes, it is critical for future research to consider follow-up analysis to gain a better idea of the impact of interventions.

Conclusion

The growth of internet and social media as a communication platform has increased the incidence of cyberbullying victimization. While there has been much research exploring the various predictors and consequences of cyberbullying victimization, most has focused on a narrow range of variables or contexts. As such, the current review aims to conduct systematic and comprehensive review of meta-analyses to reconcile literature on the various predictors and consequences of cyberbullying victimization. Findings suggest that females, school-aged populations, individuals who experienced traditional bullying and individuals who use the internet more are more likely to be cyberbullied. Unregulated school environments and unsupportive parental relationships are also associated with higher levels of cyberbullying victimization. Cyberbullying victimization is consistently associated with negative psychological outcomes such as anxiety, depression and loneliness, as well as lower school performance and maladaptive coping behaviours. The systematic identification of these robust predictors and consequences provides crucial insights that can aid stakeholders—educators, policymakers and community leaders—in developing targeted interventions that are grounded in empirical evidence. For instance, knowing specific risk factors allows for the design of prevention programmes tailored to protect vulnerable groups, while understanding the psychological impacts helps in structuring appropriate therapeutic responses. Developing such interventions is especially important, as the current review found that cyberbullying interventions show promising results. This underscores the urgent need to devote adequate resources towards developing and implementing informed evidence-based strategies to effectively combat cyberbullying victimization.

Methods

Transparency and openness

The current review was conducted in accordance with the PRISMA guidelines171. The design and synthesis plan of the current review was not pre-registered. Ethical approval was not required, as the study design (umbrella review) is exempted by the local institutional review board. Mendeley Desktop version 1.19.8 (ref. 172) was used to remove duplicates from the records obtained after the retrieval process.
In cases where effect sizes were not reported in the form of correlations, conversions were conducted using R version 3.6.3 (ref. 173). ‘effectsize’ version 0.0.6.1 (ref. 174) was used to convert Cohen’s d and odds ratios to Pearson’s r. ‘psych’ version 2.2.5 (ref. 175) was used to convert Fisher’s z to Pearson’s r. In cases where Hedge’s g was provided, it was converted to Cohen’s d using the following formula \(d=\frac{g}{\left(1-\,\frac{3}{4\left({n}_{1}+\,{n}_{2}\right)-\,9}\right)}\) (http://dlinares.org/cohend.html), where n1 and n2 refer to the sample sizes of the two groups used to calculate the effect size. The result was then converted into Pearson’s r using the ‘effectsize’ package. For meta-analyses that presented Hedge’s g and did not disclose n1 and n2, we assume that the meta-analyses included a large total sample size and treated Hedge’s g and Cohen’s d as equivalent176. Forest plots for the visualization of results were created using Microsoft Excel version 16.78 (ref. 177). The R analytic code used to convert effect sizes as well as all screening records and data extraction records of the current review are publicly available on Researchbox no. 1364 (https://researchbox.org/1364).

Study design

The current work was conducted as an umbrella review, a distinct form of systematic review designed to compile data from multiple meta-analyses addressing the same research questions9697. This approach allows for a comprehensive synthesis of evidence across studies, enhancing our understanding by comparing and contrasting results from different meta-analyses. By aggregating findings across these studies, an umbrella review helps identify patterns, strengths and gaps in the literature, providing a robust analysis of extensive datasets. This methodology is particularly suitable for fields with a vast array of studies and varying outcomes, such as cyberbullying victimization, where it can effectively distil broad insights from diverse research findings.

Search strategy

A search strategy was developed by the first author and agreed upon by the first, second and last authors to capture relevant records from each of the sources. Systematic searches were conducted by the first author on various sources for meta-analyses available up to 7 April 2024. Main sources comprised five databases (EBSCOhost ERIC, EBSCOhost PsycInfo, PubMed, Scopus and Web of Science) and 13 journals related to the field of cyberbullying (Adolescent Research Review; Aggression and Violent Behavior; Aggressive Behavior; Children and Youth Services Review; Computers in Human Behavior; Cyberpsychology, Behavior, and Social Networking; Deviant Behavior; Journal of Adolescence; Journal of Pediatric Nursing; Journal of School Violence; New Media and Society; School Psychology Review; Trauma, Violence, and Abuse). The journals were selected based on search strategies of previous meta-analyses on the topic28, as well as by selecting journals that had recently published meta-analyses on the field of cyberbullying. To augment the search, two other sources (ProQuest Dissertations and Theses, Google Scholar) were also searched to retrieve additional published literature, as well as relevant unpublished literature.
The following keywords were used to conduct the systematic literature search within the five databases: (‘meta-analy*’ OR ‘meta analy*’ OR ‘quantitative synthesis’ OR ‘review*’) AND (cyber* OR internet OR net OR online OR chat OR electronic OR mobile OR ‘social network’ OR media OR Facebook OR Twitter OR Blog* OR Youtube OR Tumblr OR Discord OR Reddit OR Instagram OR Tiktok OR Snapchat OR Pinterest OR LinkedIn) AND (harass* OR bully* OR bulli* OR victim* OR aggres* OR abus* OR maltreat* OR incivil* OR toxic* OR violen* OR delinquen* OR devian* OR ragging OR hazing OR mobbing OR intimidat*). A simplified search string containing the following keywords was used to search the relevant journals and other sources: (meta-analysis OR ‘meta analysis’ OR review) AND (cyber OR internet OR online OR ‘social media’) AND (bully OR victim).

Selection criteria

Following the literature search, the retrieved records were screened for potential inclusion independently by the first and third author or by the first author and a trained research assistant (see Fig. 8 for the PRISMA flowchart178). Any disagreements in the screening process were resolved through discussion between the two authors, and upon consensus, irrelevant and duplicate records were removed.
The PRISMA flowchart illustrate the record selection process, including the number of studies indentified or retained at eachstage of screening.
notion image
First, titles and abstracts were evaluated based on a preliminary set of criteria, which looked at whether each record (1) was published in English, Chinese, Malay or Bahasa Indonesia, (2) was a meta-analysis, (3) mentioned cyberbullying victimization and (4) mentioned at least one predictor or consequence in relation to cyberbullying victimization (94.5% overall inter-rater agreement between the first and third author and 92.76% overall inter-rater agreement between the firth author and research assistant).
Subsequently, the remaining records were assessed for inclusion based on their full-texts by the same authors and research assistant as per the following criteria (94.65% overall inter-rater agreement between the first and third author, 95.23% overall inter-rater agreement between the first author and research assistant):
  1. 1.
      2. Records were included if they were published in English, Chinese, Malay or Bahasa Indonesia.
  1. 2.
      2. Records were included if they used a meta-analytic research design.
  1. 3.
      2. Meta-analyses were included if they focused on any type of cyberbullying victimization. Cyberbullying victimization was defined as being subjected to any aggressive or bullying behaviour (for example, threatening, harassing, abusing or disrespecting) aimed directly either towards themselves or a group involving them using electronic means. Cyberbullying victimization also includes being subjected to acts such as public posts or information aimed to defame or embarrass themselves or a group they are part of. Common types of cyberbullying victimization include (but are not limited to): cyber harassment or online harassment, cyber-aggression, peer-cyberbullying victimization and cyber partner abuse and online dating violence. The records were excluded if they focused only on cyberbullying perpetration (that is, carrying out acts of cyberbullying rather than being the victim of it).
  1. 4.
      2. Meta-analyses were included if they reported at least one predictor or consequence of cyberbullying victimization.
    1. a.
        2. Common examples for predictors of cyberbullying victimization include (but are not limited to) age, gender, culture, frequency of internet use/technology use, parental monitoring, school climate and exposure to traditional bullying. Interventions aimed at preventing cyberbullying were also considered predictors, as they are designed to reduce the incidence or impact of cyberbullying and, therefore, may influence the likelihood or impact of an individual’s cyberbullying victimization experience.
    2. b.
        3. Common examples for consequences of cyberbullying includ (but are not limited to) depression, anxiety, suicidal ideation, self-esteem, loneliness and academic achievement. Consequences of cyberbullying victimization across all domains were considered (that is, not only limited to mental health outcomes but also included other outcomes, such as educational achievement and drug and alcohol use).
  1. 5.
      2. Meta-analyses were included if they examined humans. No other restrictions were placed on any sample characteristics such as age, gender, health or country.
  1. 6.
      2. Meta-analyses were included regardless of the peer review status of meta-analyses (that is, meta-analyses were included whether or not they were peer reviewed). However, if two versions of the same meta-analyses were available (for example, as part of a thesis and as part of a journal article), only the peer-reviewed version was retained.
  1. 7.
      2. Meta-analyses were included if they reported sufficient statistical information (that is, effect sizes and variance or sample size). All types of effect size were accepted. If a meta-analysis did not report the necessary information, data were requested from the relevant authors via email, ResearchGate and/or other online communication channels.

Quality assessment

The quality of each included meta-analysis was assessed independently by the first and third author or by the first author and a trained research assistant using the JBI Critical Appraisal Instrument for Systematic Reviews and Research Syntheses179. The records were evaluated using an 11-item checklist, with each item rated according to four categories (‘yes’, ‘no’, ‘unclear’ and ‘not applicable’) based on how closely the records adhered to each criterion. The criteria guiding the methodological evaluation of each record were (1) clarity of review question, (2) use of appropriate inclusion criteria, (3) use of appropriate search strategies, (4) adequacy of sources and resources to search for studies, (5) use of appropriate criteria for appraisal of studies, (6) independent critical appraisal of studies, (7) employment of methods to minimize errors in data extraction, (8) use of appropriate data synthesis methods, (9) assessment of the likelihood of publication bias, (10) have recommendations for policy and/or practice backed by data reported and (11) use of appropriate specific directives for new research. Each record was then given a quality score based on how many ‘yes’ responses were accorded (that is, the number of ‘yes’ ratings out of 11). The inter-rater agreement was generally excellent on average across all criteria, with an overall agreement rate of 96% (range 92–100%) between the first and third author and an overall agreement rate of 94% (range 90–100%) between the first author and research assistant. Any remaining discrepancies or disagreements were resolved through discussion between the reviewers.

Data extraction

The following information was independently extracted from the final list of included meta-analyses by either the first and third author or by the first author and a research assistant: author(s), year of publication, title of publication, countries and regions covered by the review, participant demographics, total number of studies, total unique sample size, cyberbullying definition and type of cyberbullying victimization measured, predictors and/or consequences of cyberbullying victimization and the relevant effect sizes denoting the association between cyberbullying victimization and the predictor and/or consequence of cyberbullying victimization explored within each meta-analysis. Regional classification of the different countries followed the listing by Wikimedia, Meta-Wiki180 (2022). Effect sizes were extracted as given within each meta-analysis without any conversions. The inter-rater agreement for all variables was generally excellent for all variables (range 77.46–100% between the first and third author and range 81.54–100% between the first author and research assistant).

Data analysis

The records included in the current review were expected to include a diverse range of predictors and consequences of cyberbullying victimization across multiple domains that were distinct from each other (for example, sociodemographic predictors, psychological predictors/consequences and behavioural consequences). Furthermore, the included meta-analyses were expected to display high levels of heterogeneity in terms of the study aims and types of cyberbullying measured. Due to these factors, it was not appropriate to synthesize results statistically. Thus, the included meta-analyses and their subsequent applicable findings were synthesized narratively by investigating the overall effect sizes denoting the association between cyberbullying victimization and the different predictors and consequences of cyberbullying victimization based on the primary findings of each meta-analysis (attempts to conduct subgroup analyses to explore heterogeneity among study results were not feasible due to an insufficient number of meta-analyses analysing identical subgroups for the same outcomes).
To better compare effect sizes, all extracted effect sizes were converted into Pearson’s r correlations by the first author. It was decided to use Pearson’s r as majority of the meta-analyses included in the current review reported correlational effect sizes (refer to ‘Transparency and openness’ for further details on the conversion process).
To synthesize associations between cyberbullying victimization and predictors of cyberbullying victimization in a theoretically appropriate manner, both predictors and consequences of cyberbullying victimization were further divided into different categories based on the domain of each variable (Table 3).
Table 3 Categorization of predictors and consequences of cyberbullying victimization analysed in the review

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All screening records of the current review are publicly available via Researchbox (https://researchbox.org/1364).

Code availability

The R analytic code used to convert effect sizes of the current review are publicly available via Researchbox (https://researchbox.org/1364).

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The authors received no specific funding for this work. We thank X. Ci Soh for her assistance in data extraction.

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Authors and Affiliations

  1. K. T. A. Sandeeshwara Kasturiratna, Andree Hartanto, Crystal H. Y. Chen & Nadyanna M. Majeed
  1. Department of Psychology, National University of Singapore, Singapore, Singapore
      2. Eddie M. W. Tong & Nadyanna M. Majeed
  1. Social Service Research Centre, National University of Singapore, Singapore, Singapore
      2. Eddie M. W. Tong

Contributions

Conceptualization was done by K.T.A.S.K. and A.H. The literature search was conducted by K.T.A.S.K. The screening of literature and data extraction was conducted by K.T.A.S.K. and C.H.Y.C. Analysis was conducted by K.T.A.S.K. Draft paper preparation was done by K.T.A.S.K. All authors contributed to reviewing and editing the paper. Visualizations were done by K.T.A.S.K. and N.M.M. Supervision was done by A.H., E.M.W.T. and N.M.M. All authors read and approved the final paper.

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Kasturiratna, K.T.A.S., Hartanto, A., Chen, C.H.Y. et al. Umbrella review of meta-analyses on the risk factors, protective factors, consequences and interventions of cyberbullying victimization. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-02011-6
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  • Received04 October 2023
  • Accepted06 September 2024
  • Published08 November 2024
  • DOIhttps://doi.org/10.1038/s41562-024-02011-6

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