Artigos

INTRODUCTION
Cyberbullying is a type of bullying that occurs through electronic devices, digital games, and social media platforms¹,². The expansion of digital sociability, driven by internet-connected devices, has intensified this phenomenon, transforming how individuals build, maintain, and end relationships through messages, calls, and image sharing³. Within the broader context of cyberculture, new forms of interaction have reshaped social dynamics, giving rise to new expressions of violence and conflict in virtual environments.
Over the past two decades, cyberbullying has become a major social and public health issue, representing a digital extension of face-to-face bullying³,?. In Brazil, digital networks are key spaces for expressions of prejudice and hostility, with girls particularly affected?,?. Cyberbullying has unprecedented consequences for adolescents’ health³,?,?, a group vulnerable due to their developmental stage, need for belonging, development of subjectivity, and increased exposure to relational difficulties and violence?. Social factors such as age¹?, race¹¹, religion¹², family structure/relationship¹³,¹?, and socioeconomic status¹? also shape this vulnerability.
Individuals may assume multiple roles: victims, aggressors or victims/aggressors, often simultaneously¹?,¹?. In Spain, 19.5% of adolescents were victims, 16.7% victims/aggressors, and 5.8% aggressors¹?. In Indonesia, 52.3% simultaneously occupied both roles¹?. These overlapping roles complicate prevention and intervention strategies.
The psychosocial effects of cyberbullying are well documented²?. Victims and perpetrators experience stress and anxiety²¹, empathy issues²², low self-esteem²³, loneliness²?, depression, and increased suicidal ideation²?. In a study of 15,506 U.S. students, victimization was associated with depression, suicidal ideation, suicidal behavior, weapon carrying, and fighting²?. Among LGBTQIA+ individuals, cyberbullying increases vulnerability to depression, suicidal ideation, and low self-esteem²?,²?. Wiguna et al.¹?, with 2,917 Indonesian students, linked cyberbullying to smoking, alcohol use, and self-harm; especially more substance use differentiates males.
Psychopathology models indicate that stressors and psychological problems interact bidirectionally in cyberbullying studies ²?-³¹. Depressive symptoms, for instance, can lead adolescents to appear withdrawn and increase the likelihood of peer rejection, while peer victimization worsens these symptoms 32-33. Consistent with this framework, a growing body of research has demonstrated that victimization by peer can heighten stigma, peer rejection, and depressive symptoms³?, increasing vulnerability to future victimization. This reciprocal dynamic aligns with the stress generation model³?, suggesting individuals contribute to the emergence of stressors through their behaviors³?-³?.
Most studies on cyberbullying and mental health investigate its effects among those attacked by digital violence or compare its impact with bullying. This study advances understanding by investigating the bidirectional association between the different roles of cyberbullying (victim, aggressor, and concomitance between victim and aggressor) and various mental health outcomes (self-esteem, psychological distress, self-mutilation, suicidal ideation, and substance use) in Brazilian adolescents. This pattern is in line with the stress generation model³?, which emphasizes that individuals do not simply endure stressful circumstances passively but may, through their actions and emotions, contribute to the emergence of stressors. This reciprocal process has also been observed in studies of cyberbullying victimization37,38. Therefore, the originality of the bidirectional approach enhances understanding of the possible associations between cyberbullying and mental health issues (and vice versa) among students, extending insights beyond traditional unidirectional analyses, although no causal inference can be drawn.

METHOD
It is a cross-sectional study with population-based and was conducted in the capitals of Campo Grande (Mato Grosso do Sul) and Vitória (Espírito Santo) in Brazil, selected based on their comparatively high prevalence of bullying among school-aged populations in Brazil39. Data collection occurred in 2018 and included second-year high school students enrolled in both public and private schools in the two municipalities. Ethical approval was obtained from the Research Ethics Committee of the National School of Public Health, Fiocruz (CAAE: 58943916.3.0000.5240). Participation was voluntary, with informed consent provided by parents or guardians and assent obtained from the students.

Study Population
The study was designed to produce representative estimates for 2nd-grade high school students enrolled in public and private schools in the two selected capitals. Multistage cluster sampling was used, with 5% precision, 95% confidence level, and an expected prevalence of victimization of 20%. The following steps were applied: (1) stratification by school system (public/private); (2) selection of schools with probability proportional to size (PPT) for the 2nd grade; and (3) random selection of one class per school, including all students in that class. A total of 480 students (242 from Campo Grande and 238 from Vitória) responded to the questionnaire. All student data were weighed according to the calculated sampling weight, considering all selection stages, which allowed the analysis of "expanded" estimates for the entire population of interest40.

Instruments
A self-administered questionnaire was used, which included the following scales and items.
Sociodemographic profile: sex (male, female), age (13-16 and 17-19 years), skin color or race (white, black/brown, indigenous/yellow), sexual practice (heterosexual, homosexual/bisexual); family structure (living with father and mother, father/stepmother or mother/stepfather, with only one parent), religion (practices some religion or participates in some religious practice); and social stratum of the family, estimated at according to family assets and the education of the head of the family, resulting in A/B stratum (wealthiest social strata) and C/D/E (middle and lower social strata)41.
Mental health outcomes: (1) Rosenberg’s Self-Esteem Scale42,43, composed of 10 statements evaluating positive and negative self-attitudes, with response options ranging from “totally agree” to “totally disagree.” Higher scores indicate greater self-esteem. In its adaptation for the Brazilian population, the scale demonstrated a Cronbach’s alpha of 0.68, an intraclass correlation coefficient (ICC) of 0.70, and moderate kappa values. It was analyzed as a continuous variable; (2) Self-Report Questionnaire (SRQ-20)44,45, which measures mental distress in adolescents. Each positive response scores one point, with cut-off values of 7 for men and 8 for women45; (3) self-harm, assessed by asking whether the participant had intentionally injured themselves (e.g., cutting, burning, scratching the skin, hitting objects, biting); (4) suicidal ideation, measured by the statement: “I was so sad that I seriously thought about ending my life”; and (5) substance use, evaluated through questions such as “I drank alcohol until I got drunk or felt drunk,” “used marijuana, cocaine, crack or ecstasy,” and “used tranquilizers.”
Revised Cyberbullying Inventory (RCBI)46 was applied to assess the frequency with which the adolescent performed or suffered forms of digital aggression in the last six months on digital social network platforms. The scale has 14 items that allow responses ranging from never to more than three times. Each item was answered twice, considering the frequency of events related to cyber aggression ("I did that"), cyber victimization ("That happened to me"), and both (aggressor and victim)47,48. At least one episode of cyber aggression, cyber victimization, and both characterizes the behavior studied48. Wendt48 adapted the scale for Brazilian adolescents (Cronbach's alpha of 0.75 for cyber aggression and 0.76 for cyber victimization). The Brazilian most used social networks were included in the scale, such as Instagram, direct from Instagram (private messages), and communication apps like WhatsApp.

Data analysis
Initially, a descriptive analysis of sociodemographic variables was performed according to the cyberbullying roles. Relative frequencies and their respective 95% confidence intervals were calculated. The chi-square test was applied to verify the difference between the proportions, and those whose p-value results were less than or equal to 0.05 were considered significant.
To estimate the association between cyberbullying roles and each mental health outcome, a logistic model for complex samples was adjusted for the outcome. In each model, cyberbullying roles were included as a four-level categorical variable, adopting "no cyberbullying" as the reference category. Adjusted odds ratios (ORs and 95% CIs) were calculated, controlling for sex, age, race/ethnicity, family structure, social class, and religion. This approach was used to determine whether involvement in different cyberbullying roles influences the likelihood of mental health problems.
To investigate the relationship in the opposite direction, that is, whether mental health problems impact the chance of involvement in cyberbullying roles, three separate binary comparisons were considered as outcomes: aggressor versus without cyber, victim versus without cyber, and victim/aggressor versus without cyber. In each of these comparisons, separate models were adjusted, including only one mental health variable at a time as the primary predictor (depression, low self-esteem, SRQ-20, suicidal ideation, self-harm, drug use), always adjusting for the same covariates (sex, age, race/ethnicity, family structure, social class, and religion). This strategy sought to assess whether the presence of mental health problems is associated with a greater chance of being a perpetrator, a victim, or both. A bidirectional approach based on cross-sectional studies has been investigated in various countries and contexts in the investigation of associations 49-51. The results should be evaluated with caution due to the impossibility of causal interpretation.
The fit of the models was compared using Pseudo-R52-54. All estimates were obtained with the inclusion of a sampling design and were performed in SPSS 24, Complex Samples module, and using the final expansion weight. The design and sample weights were considered in all analyses. Crude and adjusted odds ratio (OR) and confidence intervals indicate the results obtained.

RESULTS
Most participants assume the role of victim and aggressor simultaneously (72.9%), with a much lower percentage for those who identify as only aggressors (10.9%) or only victims (6.7%), and a portion (9.5%) of respondents reported not having been involved in cyberbullying experiences. An analysis of cyberbullying roles according to the adolescents' sociodemographic profile shows that the distributions were very similar across most of the items investigated, with no statistically significant differences. Only family structure showed a statistically significant association (p=0.027). Adolescents in "other" family arrangements, i.e., those whose parents do not live together, had a higher proportion in the "aggressor" role (15.6% vs. 6.1% among those living with both parents) and a lower proportion "without cyberbullying" (7.9% vs. 11.7%), maintaining high values in "both" (71.6% vs. 75.0%) (Table 1).

Tab.1

A consistent association was observed between involvement in cyberbullying, especially victimization, and worse mental health indicators, after adjusting for sex, age, family structure, color/race, religion, and social class (Table 2). For low self-esteem, the odds were markedly higher among victims (OR=14.40; IC95% 4.77-43.46) and between aggressors/victims (OR=5.33; IC95%1.55-18.31), while aggressors showed an increase in borderline (OR=4.59; IC95% 0.98-21.51). In psychological distress, the pattern repeats itself: victims (OR=5.53; IC95%1.25-24.39) and aggressors/victims (OR=5.68; IC95%1.84-17.54) had a higher chance of the outcome; for aggressors, the association was smaller and not significant (OR=2.34; IC95%0.65-8.40). In drug use, there was a gradient according to the role in cyberbullying: aggressors (OR=2.19; IC95%1.13-4.22), victims (OR=3.98; IC95%1.26-12.66) and aggressors/victims (OR=4.29; IC95%2.59-7.09), all with a greater chance of development compared to those who were not involved (Table 2).
For self-harm, the increase was clear among victims (OR=3.35; IC95%1.39-8.07); the estimates for aggressors/victims and aggressors did not reach statistical significance. Finally, for suicidal ideation, robust associations were observed for victims (OR=8.33; IC95%2.89-23.81) and aggressors/victims (OR=5.10; IC95%2.23-11.63), while for aggressors the statistic was not significant. In summary, roles that include victimization and aggressor/victim concentrated the greatest chances of adverse outcomes, whereas the isolated aggressor role presented weaker or inconsistent results (except for drug use).

Tab. 2

Table 3 shows that, compared to adolescents with low self-esteem, those without low self-esteem had lower odds of involvement in cyberbullying across all roles: aggressor (OR=0.31; 95%CI 0.11-0.86; p=0.027), victim (OR=0.04; 95%CI 0.00-0.34; p=0.008) and both (OR=0.18; 95%CI 0.05-0.59; p=0.008), suggesting an association between low self-esteem and participation in the phenomenon. For psychic suffering, positive and statistically significant associations were observed for the victim (OR=6.93; 95%CI 1.29-37.14; p=0.027) and for both (OR=5.70; 95%CI 1.79-18.17; p=0.006), but not for the aggressor.
Self-harm was associated with the victim role (OR=4.47; 95%CI 1.16-17.16; p=0.032), with no evidence for aggressor or both. Suicidal ideation showed the highest magnitudes: victim (OR=11.83; 95%CI 2.48-56.59; p=0.005) and both (OR=5.65; 95%CI 2.51-12.69; p<0.001), while there was no association for aggressor (p=0.478). Drug use was consistently associated with all roles: aggressor (OR=2.73; 95%CI 1.17-6.36; p=0.024), victim (OR=5.75; 95%CI 1.21-27.33; p=0.031) and both (OR=4.07; 95%CI 2.35-7.06; p<0.001). All models were adjusted for sex, family structure, age group, color/race, religion and social class (Table 3).

Tab. 3

DISCUSSION
The study identified associations between involvement in cyberbullying and problems mental health indicators, with distinct patterns observed among victims, aggressors, and both roles55-58. Adolescents reporting no involvement exhibited lower prevalence of mental health issues across all indicators examined, whereas those classified as both victim and aggressor showed the highest levels of vulnerability, including substance use and suicidal ideation. Although cross-sectional design precludes causal inference, the findings suggest an association of cyberbullying experiences and psychosocial distress and vice-versa. Cyber victims presented elevated levels of internalizing symptoms, in line with prior research linking victimization to including low self-esteem, suicidal ideation, non-suicidal self-injury, and the use of illicit substances59-61. Feelings of humiliation, helplessness, isolation, and exclusion contribute to, and intensify, psychological distress. The possibility that pre-existing mental health difficulties increase susceptibility to victimization points to association and indicates that these relationships may be reciprocal or mutually reinforcing 61,62.
A growing body of longitudinal evidence suggests that certain mental health indicators may precede and be associated with increased likelihood of cybervictimization, findings that are consistent with the stress generation model in digital contexts 38,40,41. Adolescents exhibiting sadness, social withdrawal, or emotional vulnerability may be perceived as less socially integrated, potentially increasing their exposure to online aggression. In turn, experiences of victimization may coexist with intensified feelings of worthlessness, contributing to a self-reinforcing cycle of psychosocial distress34.
The characteristic of the anonymity or the proximity to the perpetrator, the permanence of the offenses in the online space and the memory of the potential audience, the frequency of the attacks, and the passivity in receiving the offenses, not reacting, and not sharing the situation and the suffering, may amplify the impact for those who are just victims of cyberbullying 61,63. Moreover, reliance on digital connectivity may lead some adolescents to tolerate adverse online experiences rather than disengage from social platforms63. The uncertainty surrounding the perpetrator’s identity can intensify distress, although victimization involving known or socially proximal aggressors may also carry significant emotional consequences. Conversely, platform-based content removal mechanisms and supportive bystander responses appear to mitigate stress and buffer negative mental health effects64.
One of the persistent gaps in cyberbullying literature concerns perpetrators. Compared to victims, little is known about their sociodemographic and mental health characteristics65,66 or whether perpetration is associated with other forms of aggressive behaviors67. Existing evidence is heterogeneous: while some studies report elevated levels of stress, depressive symptoms, anxiety, and suicidal behaviors among perpetrators24,68, others identify weak or null associations with peer difficulties or diminished well-being65. These findings suggest that the psychosocial correlates of perpetration may not mirror those of victimization. The affordances of digital environments, such as perceived anonymity and reduced immediate social accountability, may facilitate identity experimentation and disinhibited behavior without direct exposure to social judgment69,70. Additionally, individual characteristics, including personality traits, normative beliefs about aggression, and experiences of social marginalization, have been proposed as potential correlates of online aggressive behavior68.
Among cyber victim-aggressors, the mental health profile observed in this study more closely resembled that of victims, although with comparatively lower magnitude of association. This finding is noteworthy, as one might expect individuals occupying both roles to exhibit a cumulative burden derived from simultaneous exposure to victimization and perpetration71. One possible interpretation is that engagement in aggressive behavior may, in some cases, operate as a reactive or defensive response to digital attacks, potentially attenuating part of the perceived harm. Nonetheless, indicators such as low self-esteem, psychological distress, suicidal ideation, and illicit substance use remained prevalent within this group, suggesting persistent psychosocial vulnerability.
Another important point is the high prevalence of respondents reporting involvement in cyberbullying, consistent with studies using the same measurement instrument: 72.7% of participants in Porto Alegre, Brazil reported at least one incident of cyber aggression and 75.6% reported at least one episode of cyber victimization 72; 72% of adolescents in the United States reported at least one event related to the phenomenon73; and 65% of adolescents in Canada and China reported similar experiences74. Although high prevalence rates have been observed across different countries, cultural differences warrant further investigation, as do comparative studies employing different measurement instruments.
Potential mechanisms linking cyberbullying and mental health need consideration. Variables such as sex and gender, family and peer relationships, social support, and emotional regulation are theoretically relevant and were accounted for in the analyses75-76. Prior research suggests that gender shapes the internalization or externalization of digital violence, while difficulties in emotional regulation and help-seeking may contribute to the association between victimization and suicidal ideation. Other studies point to school engagement and deviant peer affiliation as possible intermediary pathways, particularly in relation to substance use19,77,78.
These findings highlight the need for prevention programs and strategies addressing distinct roles in cyberbullying (victim, perpetrator, or both). Gaffney et al.79, in a meta-analysis, found that anti-cyberbullying programs can reduce perpetration by 10%-15% and victimization by 14%. Interventions involving empathy and resilience training, educational campaigns, and adolescent participation are promising strategies80-82.
The findings should be interpreted considering several limitations. All measures were based on self-reports, which may introduce recall and reporting bias, although the use of validated instruments enhances reliability. Restricting the sample to school-enrolled adolescents may limit generalizability, and self-report data may underestimate or overestimate certain behaviors. Moreover, the cross-sectional design precludes establishing temporal order or causal inference. The inverted model specifications were conducted as exploratory analyses, and the associations observed may reflect correlational relationships or conceptual overlap between cyberbullying experiences and self-reported mental health indicators. Thus, while the results suggest bidirectionality, they may also indicate co-occurrence and shared underlying determinants. Caution is therefore warranted given the study’s cross-sectional nature. Although categories were aggregated to facilitate analysis, some cross-tabulations yielded wide confidence intervals. Future research should include larger samples to enable more robust analyses. Despite these limitations, the representativeness of the sample in two Brazilian cities with high bullying rates and the societal relevance of the topic underscore the importance of this study.
This study should thus be understood as exploratory and descriptive, contributing to the identification of mental health correlates of cyberbullying rather than testing causal mechanisms. Given the relative scarcity of epidemiological studies on this topic in Brazil, comparisons remain limited. Future research should employ longitudinal designs to clarify directionality, examine frequency and severity of cyberbullying, assess the role of pre-existing mental health conditions, parental digital supervision, and explore potential mediating and moderating effects of gender and sex.

Final considerations
The possible circular relationship between cyberbullying and mental health indicated by the data points to a dynamic process in which psychological distress may both increase vulnerability to online victimization and be intensified by it, generating self-reinforcing cycles of harm. Practically, these findings highlight the need for integrated public policies that move beyond reactive and punitive approaches, promoting coordinated mental health screening, early identification of at-risk youth, and digital literacy initiatives. At the school level, they support the adoption of whole-school prevention frameworks. Prevention strategies should be multi-tiered: universal interventions to foster resilience and safe online behavior; selective programs targeting students with emerging emotional difficulties; and indicated mental health services for those already affected. Future research on the bidirectional relationship between cyberbullying and mental health may assist policymakers and educators in developing more effective, evidence-informed strategies that simultaneously reduce victimization and strengthen psychological well-being.

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