ArticleBMC psychology2026
Cyberbullying in college: influencing factors and patterns of victimization.
Article in BMC psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Latent class/profile analysis in research on school bullying behavior patterns: a systematic review.Frontiers in public health · 2026Pooled it
- Profiles of cybervictimization among college students and associated psychosocial patterns: a latent profile and network analysis.Frontiers in psychology · 2026Article
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Authors and funding
6 authors.
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Abstract
backgroundWith the rapid advancement of social media and online communication platforms, cyberbullying has become increasingly rampant. As college students transition into adulthood, they may lack the maturity to navigate the risks of using such platforms, rendering them susceptible to cyberbullying and its associated harms. As a result, this study aimed to investigate the patterns of cyberbullying victimization among college students, analyze the influencing factors associated with these patterns, and provide a basis for adopting targeted strategies to address the issue. The objective of this was to derive insights that could inform the development of targeted coping strategies.
methodThis study was conducted under the framework of Routine Activity Theory and Lifestyle Theory. A questionnaire was designed and distributed to college students. The analyses empirically investigated the patterns of cyberbullying victimization among college students using Latent Class Analysis (LCA). Potential predictors of class membership were also examined using multinomial logistic regression analysis.
resultA sample of 414 college students (55.1% females, age M = 21.22, SD = 1.64) from China's mainland participated in this study. Three patterns of victimization were identified: "all-types victimization" (21.8%), "verbal-only victimization" (25.1%), and "low victimization" (53.1%).
conclusionThe findings regarding influencing factors indicated that: (1) differences among the three classes, in terms of gender and year of study, were statistically significant (P < 0.05); and (2) social media use and self-disclosure increased cyberbullying victimization, while the social support received by students decreased cyberbullying victimization. These results can assist social media platform owners and policymakers with the development of targeted strategies for cyberbullying prevention.
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