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Background: Cyber victimization exposes individuals to numerous risks. Developmental and psychological factors may leave some users unaware of the potential dangers, increasing their susceptibility to psychological distress. Despite this vulnerability, methods for identifying those at risk of cyber victimization within health care settings are limited, as is research that explores their experiences of cyber victimization. The purpose of this study was to analyze how users describe experiences of cyber victimization on the social media platform Reddit (Reddit, Inc) using data mining.
Objective: This study aimed to analyze and describe how users on Reddit describe and discuss their experience of cyber victimization using data mining and computational analysis of unsolicited data.
Methods: This computational qualitative study used data mining, Word Adjacency Graph (WAG) modeling, and thematic analysis to analyze discussions of Reddit users surrounding cyber victimization. Inclusion criteria included posts from 2012 to 2023 from subreddits r/cyberbullying and r/bullying. GPT-4 (OpenAI), an advanced artificial intelligence language model, summarized posts and assisted in cluster labeling. Posts were reviewed to remove irrelevant content and duplicates. User anonymity was maintained throughout the study.
Results: A total of 13,381 posts from 3283 Reddit were analyzed, with approximately 5.1% (n=678) originating between 2012 and 2018 and 94.9% (n=12,703) from 2019 to 2023. The WAG modeling approach identified 38 clusters, with 35 deemed to be relevant to cyber victimization experiences. Two clusters containing irrelevant material were excluded. Six overarching themes emerged: (1) psychological impact, (2) coping and healing, (3) protecting yourself online, (4) protecting yourself offline, (5) victimization across various settings, and (6) seeking meaning and understanding.
Conclusions: The study highlights the effectiveness of data mining and AI in analyzing large public datasets for qualitative research. These methods can inform future studies on risky internet behavior, victimization, and assessment strategies in health care settings.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12407219 | PMC |
http://dx.doi.org/10.2196/75493 | DOI Listing |
J Sch Health
September 2025
Developmental, Social, and Health Psychology, University of Kentucky, Lexington, Kentucky, USA.
Background: As students' use of mobile devices during school hours continues to increase, cyberbullying and online sexual harassment now occur during school hours, on school grounds via personal devices. Despite this growing reality, there is little knowledge about secondary school efforts to address it.
Methods: To understand what is needed to construct or reform policies that reflect students' online experiences, we used a mixed method approach to identify and analyze language (e.
Arch Psychiatr Nurs
October 2025
Inamdar Multi-specialty Hospital, Pune, Maharashtra, India.
Prev Med Rep
October 2025
Boston College, School of Social Work, 140 Commonwealth Avenue, Chestnut Hill, MA 02467, USA.
Objective: We aimed to examine the impact of cyberbullying and off-campus cyberbullying provisions in state anti-bullying laws on cyberbullying and whether the effects varied by sexual minority status.
Methods: Using data from the 2011-2021 Youth Risk Behavior Surveys (911,086 high school students in 44 states in the United States), we estimated difference-in-differences logistic regression models. Policies were categorized into three types: "strong" (including cyberbullying and off-campus provisions); "moderate" (cyberbullying provisions only); or "neither" (neither provision).
Studies have shown anxious-ambivalent attachment commonly characterizes cyberbullying victims. Similarly, studies have shown an association between anxious-ambivalent attachment and problematic internet use (PIU). Previous studies have explained this asso-ciation by noting the high interpersonal needs of individuals with anxious-ambivalent attachment who are looking for social contact.
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