The Impact of Cyberbullying Victimization on Cyberbullying Among Chinese Junior High School Students: A Longitudinal Moderated Mediation Model

Abstract

Research suggests that cyberbullying is more harmful than traditional bullying and can cause more profound harm to individuals. The study aimed to examine the effects of cyberbullying victimization on junior high school students’ cyberbullying over time while exploring the mediating role of loneliness and the moderating role of perceived social support. Four self-report questionnaires were administered to 561 middle school students at three time points. Data were analyzed using SPSS for descriptive statistics and Pearson correlation analysis. Moderated mediated effects tests were performed on the variables using SPSS Process. T1 cyberbullying victimization in middle school students predicts T2 cyberbullying behavior. T2 loneliness mediates the role of T1 cyberbullying victimization in influencing T2 cyberbullying. T2 perceived social support moderated the second half of the mediating role pathway. First, there are many mediators and moderators of cyberbullying victimization that affect cyberbullying, and others need to be examined. Second, only three middle schools in China were selected as investigators for this study, and future studies could select different populations to verify the applicability of the findings. Cyberbullying victimization can increase cyberbullying. Reducing cyberbullying requires attention to psychological and behavioral changes in cyberbullying victims.

Keywords

cyberbullying victimization loneliness social support moderated mediation model longitudinal study

Introduction

With the continuous development of the electronic information industry, minors’ access to the Internet is gradually increasing, which also increases the risk of problematic Internet behavior. According to the 5th National Survey Report on Internet Usage by Minors released by China Internet Network Information Center (2023), the number of underage Internet users exceeded 193 million in 2022. In terms of youth Internet security, 27.6% of underage Internet users said they had encountered Internet security incidents in the past 6 months.

Cyberbullying is defined as a hostile and aggressive behavior toward an individual or group of individuals through the use of electronic messages and other means (Smith et al., 2008). Due to the lack of time and space limitations, cyberbullying behavior is more likely to occur and more people are affected by cyberbullying compared to traditional bullying. Existing research has shown that cyberbullying is more harmful than traditional bullying(Perren et al., 2010).

Currently, research on bullying in China mainly focuses on traditional bullying (school bullying), with less research on cyberbullying (Chan & Wong, 2015). Meanwhile, existing studies have mainly focused on exploring the effects of cyberbullying on mental health (Li et al., 2024; Zhu et al., 2021), and further research is needed on how cyberbullying victimization affects individual behavior. The results on cyberbullying still need to be further validated in China. The present study adopted a longitudinal research design to examine the effects of cyberbullying victimization on junior high school students’ cyberbullying over time through regression analysis while exploring the mediating role of loneliness and the moderating role of navigating social support.

Cyberbullying Victimization and Cyberbullying

The Frustration-Aggression Hypothesis suggests that frustration is an important reason to explain the emergence of aggressive behavior, and when a person faces frustration, it correspondingly increases the individual’s aggressive behavior (Nickerson, 2021). Related studies have shown that there is a link between cyberbullying victimization and cyberbullying and that individuals who have been bullied are prone to choosing revenge and transforming from bullying victims to bullies (Zych et al., 2019). Research has shown that cyberbullying victimization is a significant predictor of cyberbullying behavior (Kowalski et al., 2016). Cyberbullying victimization positively predicts cyberbullying (Camacho et al., 2021).

Loneliness as a Mediator

Existing research suggests that cyberbullying victimization triggers a range of psychological problems such as anxiety, insomnia (L. Wang & Ngai, 2020), depression (Selkie et al., 2015), and even suicidal ideation and behavior (Rodelli et al., 2018). The development of cyberbullying is related to the individual’s perception of negative emotions (Onishi et al., 2012). Research on predictors of cyberbullying found that negative emotions positively predicted cyberbullying behavior (Patchin & Hinduja, 2010). Loneliness impairs an individual’s interpersonal relationships, and individuals in poor relationships are more likely to perceive neutral behaviors as aggressive, resulting in cyberbullying (Olenik-Shemesh & Heiman, 2017). Loneliness may mediate between online cyberbullying victimization and cyberbullying behavior.

Perceived Social Support as a Moderator

Perceived social support is a cognitive factor that assesses the extent to which a person is respected, supported, and understood in social relationships (Singstad et al., 2020). Perceived social support not only predicts and promotes healthy development (Brissette et al., 2002) but is also an important coping resource for individuals to deal with adverse external stimuli (Rosenstock, 1974). Low levels of perceived social support may be a risk factor for negative emotions and maladjustment (Seeds et al., 2010). Previous research has shown that social support is a protective factor for cyberbullying behavior (Arató et al., 2022; Chu et al., 2021). Low levels of peer support (Baldry et al., 2015; Heerde & Hemphill, 2018) and family support (Calvete et al., 2010; Fanti et al., 2012) may increase the likelihood that individuals will engage in cyberbullying. Therefore, perceived social support may play a moderating role in loneliness influencing cyberbullying.

The Present Study

The current study tested the following hypotheses.

Hypothesis 1: T1 cyberbullying victimization significantly positively predicted T2 cyberbullying.

Hypothesis 2: T1cyberbullying victimization may affect middle school students’ T2 cyberbullying through the mediating effects of T2 loneliness.

Hypothesis 3: T2 social support moderated the second half of the path of the mediating effect.

Methods

Participants and Procedure

The study was approved by the Ethics Committee of Shaoxing University. Students from three middle schools in Shaoxing City were selected for the study, and a 10-month follow-up survey was conducted using the convenience sampling method. The first survey was conducted in May 2023 and 656 valid data were collected. The second survey was conducted in October 2023, and a total of 583 data were collected. The third survey was conducted in March 2024 and 576 data were collected. Excluding invalid questionnaires (response time less than 2 SD), a total of 561 junior high school students (females = 52.0%, mean age = 13.35, SD = 0.71) participated in the survey.

Measures

Cyberbullying Victimization

The Cyberbullying Victimization Scale developed by Shapka et al. (2017) and revised by Xie et al. (2022) was used. The scale consists of six items (e.g., “Personal information you do not want others to know about you has been uploaded or reposted online”). Each item is rated on a 5-point Likert-type scale (1 = never, 5 = always). The total scores of all items were averaged, with higher scores representing higher levels of cyberbullying victimization. The Cronbach’s alpha for this scale in this study was 0.70.

Perceived Social Support Scale

The Multidimensional Scale of Perceived Social Support (MSPSS) developed by Zimet et al. (1988) and revised by Wang et al. (1999) was used. The scale consists of 12 items (e.g., “There are people in my life [teachers, classmates, relatives] who care about my feelings”). The scale contains three dimensions: family support, friend support, and other support. Each item is rated on a 7-point Likert-type scale (1 = completely disagree and 7 = completely agree). The total scores of all items of the scale were averaged, with higher scores representing higher levels of perceived social support. The Cronbach’s alpha for this scale in this study was 0.93.

Loneliness

The ULS-8 Loneliness Scale revised by Hays and DiMatteo (1987) was used to measure the level of loneliness among middle school students. The scale consists of eight items (e.g., “I feel left out”). Each item is rated on a 4-point Likert-type scale (1 = never, 4 = always). The total scores of all items were averaged, with higher scores indicating higher levels of loneliness. The Cronbach’s alpha for this scale in this study was 0.73.

Cyberbullying

The Cyberbullying Scale developed by Shapka et al. (2017) and revised by Xie et al. (2022) was used. The scale consists of six items (e.g., “Posted or re-posted something embarrassing or mean about another person online?”). Each item is rated on a 5-point Likert-type scale (1 = never, 5 = always). The total score for all items was averaged, with higher scores representing higher levels of cyberbullying. The Cronbach’s alpha for this scale in this study was 0.91.

Covariates

According to previous studies, gender and age are important factors that influence cyberbullying victimization and cyberbullying (Calvete et al., 2010). Therefore, gender and age were set as control variables in this study.

Data Analysis

This study was analyzed using SPSS 25.0. First, Harman’s single-factor test was taken to examine the common method bias. The results showed that there were 6 factors with eigenvalues > 1. The first factor accounted for 26.06% of the total variance, which was below the threshold of 40%. Therefore, there was no serious common methodological bias in the study analysis. Second, descriptive and correlation analyses were performed for all variables. Third, the mediating role of loneliness and the moderating role of perceived social support were tested using SPSS PROCESS models 4 and 14 respectively. Based on bootstrap random samples (n = 5,000), effects were considered significant when the 95% confidence interval (CI) did not include 0.

Results

Descriptive Statistics

T1 cyberbullying victimization was significantly negatively correlated with T2 perceived social support (r = −0.21, p < .01), and significantly positively correlated with T2 loneliness (r = 0.18, p < .01) and T2 cyberbullying (r = 0.16, p < .01). T2 perceived social support was significantly negatively correlated with T2 loneliness (r = −0.31, p < .01) and T2 cyberbullying (r = −0.13, p < .01). T2 loneliness was significantly positively correlated with T2 cyberbullying (r = 0.28, p < .01). This result supported Hypothesis 1.

Testing for Mediation Effects

We used PROCESS’s Model 4 to test whether T2 loneliness mediated the link between T1 cyberbullying victimization and T2 cyberbullying. After controlling for gender and age, results showed (see Table 1) that T1 cyberbullying victimization was positively associated with loneliness (β = 0.18, p < .001), which in turn was positively associated with cyberbullying (β = 0.25, p < .001). The residual direct effect was significant (β = 0.10, p < .05). Thus, T2 loneliness partially mediated the relationship between T1 cyberbullying victimization and T2 cyberbullying (indirect effect = .05, Boot SE = .02, 95% CI = [0.02, 0.08]). This result supported Hypothesis 2.

Table 1.

Testing the Mediation Effects of Cyberbullying Victimization on Cyberbullying.

Independent variables	Model 1 (criterion T2 loneliness)		Model 2 (criterion T2 cyberbullying)
Independent variables	β	T	β	t
CO: gender	.07	1.69	.05	1.19
CO: age	.02	0.44	.10	2.48*
X: T1 cyberbullying victimization	.18	4.18***	.10	2.51*
ME: T2 loneliness			.25	6.10***
R ²	.04		.10
F	7.46***		15.53***

Note. N = 561. All variables were standardized.

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

Testing for Moderated Mediation Effect

In addition, Model 14 of PROCESS was used to test the moderated mediation hypothesis that there is moderation. As shown in Table 2, in Model 1, there was a significant effect of T1 cyberbullying victimization on T2 loneliness, β = 0.33, p < .001. Model 2 showed that the effect of T1 cyberbullying victimization on T2 cyberbullying was significant, β = 0.10, p < .001. The effect of T2 loneliness on T2 cyberbullying was significant, β = 0.74, p < .001, and T2 social support moderated this effect, β = −0.12, p < .001.

Table 2.

Testing the Moderating Effects of Cyberbullying Victimization on Cyberbullying.

Independent variables	Model 1 (criterion T2 loneliness)		Model 2 (criterion T2 cyberbullying victimization)
Independent variables	β	T	β	t
CO: gender	.07	1.69	.03	1.63
CO: age	.01	0.44	.04	2.81**
X: T1 cyberbullying victimization	.33	4.18***	.10	2.67**
ME: T2 loneliness			.74	8.00***
MO: T2 social support			.17	5.96***
MEMO: T2 loneliness × T2 social support			−.12	−7.02***
R ²	.04		0.18
F	7.46***		20.04***

Note. N = 561. Each column is a regression model that predicts the criterion at the top of the column. Gender was dummy-coded. All variables were standardized. CO = control variable; X = independent variable; ME = mediator; MEMO = interaction between mediator and moderator.

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

To facilitate the interpretation of the moderating role of T2 social support, a plot of the relationship between T2 loneliness and T2 cyberbullying at two levels of T2 social support (M + 1SD and M − 1SD) is presented in Figure 1. A simple slope test showed that for low social support individuals (M − 1SD), higher loneliness was associated with higher cyberbullying, β_simple = 0.19, p < .001. Whereas, for high social support individuals (M + 1SD), higher loneliness was associated with lower cyberbullying, β_simple = −.07, p < .05. Bias-corrected bootstrap analyses (Table 3) further indicated a significant effect between loneliness and cyberbullying for adolescents with low levels of social support (β = .06, SE = .04, 95% CI = [0.0, 0.15]. Among adolescents with higher levels of social support, the effect between loneliness and cyberbullying was not significant (β = −.02, SE = .03, 95% CI = [−0.09, 0.02]). The index of moderated mediation is −.04 (95% CI = [−0.11, −0.01]). The confidence interval does not contain 0, indicating a significant mediation effect with moderation. This result supports Hypothesis 3.

Table 3.

Mediating Effects at Different Levels of T2 Perceived Social Support.

Level	Effect	SE	95% CI
Level	Effect	SE	Lower	Upper
M – 1 SD	0.06	0.04	0.01	0.15
M	0.20	0.01	0.01	0.05
M + 1 SD	−0.02	0.03	−0.09	0.02

In summary, the association between T1 cyberbullying victimization and T2 cyberbullying is mediated by T2 loneliness, whereas the indirect effect of T1 cyberbullying victimization on T2 cyberbullying through T2 loneliness is moderated by T2 perceived social support.

Discussion

Teenagers are at a critical stage of physical and mental development, and their cognitive abilities are still in the process of continuous development and change. They are easily induced by undesirable information in the complex and changeable network environment and become cyberbullying victims and cyberbullies. Adopting the paradigm of tracking research, this study explored the influence of cyberbullying victimization on cyberbullying behaviors of middle school students and its internal mechanism through regression analysis.

This study found that baseline cyberbullying victimization positively predicted cyberbullying among middle school students after controlling for the role of gender and age, and that cyberbullying victimization increased cyberbullying among middle school students. This suggests that individuals who are victimized by cyberbullying are more likely to shift their identity to become cyberbullying perpetrators. This is consistent with the findings of previous studies (Kowalski et al., 2016). Individuals who are bullied can retaliate and attack others online (Fernández-Antelo & Cuadrado-Gordillo, 2018). Thus when an individual is a victim of bullying, he or she is more likely to undergo an identity shift to become a bully, which is somewhat stable across time.

This study found that loneliness mediates the process by which cyberbullying victimization affects cyberbullying. Existing research suggests that adolescents who experience cyberbullying exhibit depression (Selkie et al., 2015) and a range of emotional problems (Bannink et al., 2014). The present study is consistent with the findings of existing research. Loneliness as a negative emotion and cyberbullying victimization increases loneliness. The loneliness model suggests that loneliness causes individuals to perceive their social environment as a threatening place, which in turn leads to more negative social interactions (Cacioppo et al., 2006), such as bullying behavior (Asher & Paquette, 2003).

The present study also found that an individual’s level of perceived social support moderated the second half of the path of the mediation process. Specifically, the relationship between loneliness and cyberbullying became stronger for middle school students with low levels of perceived social support compared to those with high levels of perceived social support. Thus, it can be seen that perceived social support can protect individual mental health and help reduce the emergence of cyberbullying behavior. Existing research has proved that high levels of perceived social support can help individuals cope with adverse external stimuli (Rosenstock, 1974), and low levels of perceived social support may lead to maladaptation (Seeds et al., 2010).

The findings not only reveal how cyberbullying victimization affects the emergence of cyberbullying behaviors but also illustrate the change of this effect over time. The study still has some limitations that can be improved in future research. First, the mechanism of cyberbullying behavior is complex, and this study focuses on the roles of cyberbullying victimization, perceived social support, and loneliness, while other individual factors, such as self-esteem and sense of hope, still need to be further investigated. Secondly, only students from three middle schools were selected for this study, and we may try to expand the sample to verify the results in the future. This study can provide guidance and suggestions for regulating social network use behavior and reducing cyberbullying.

Footnotes

Data Availability Statement

Data available on request from the authors.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the 2025 Research Topics of Zhejiang Federation of Humanities and Social Sciences Circles (2025N155) and the Research project of Shaoxing University (2024SK0009).

Ethical Approval

This study was performed in accordance with the Declaration of Helsinki. Participants were informed of the purpose of the study before the survey and told they could refuse to answer at any time. Participants expressed informed consent prior to participating in the survey.

ORCID iD

Jing Wu

Author Biographies

Jing Wu received the B.S. degree in Sociology from Shanxi Medical University, China, in 2007, the M.S. degree in Psychology from Shaanxi Normal University, China, in 2014 and the Ph.D. degree in Philosophy from Jilin University, China, in 2020. Her research interests include bullying on campus and mental health of primary and secondary school students.

Yuan Gao and Qianxiu Xiao is currently working toward the M.S. degree in Psychology with the Department of psychology, Shaoxing University, China.

Xu Zhang received the M.S. degree in Psychology from Shaoxing University, China, in 2024.

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