AI Article Synopsis

  • Social media platforms like Twitter and Weibo can promote both positive and negative interactions, including an increase in cyberbullying during public crises, and the study examines how users' gender and account verification impact these behaviors.
  • Utilizing various theoretical frameworks, the study analyzed posts on Weibo to distinguish between pro- and anti-cyberbullying content, focusing on the emotional expressions related to anger.
  • Results indicated that pro-cyberbullying posts were richer in emotional content, with verified female users showing higher instances of anger-related expressions compared to others, providing insights for future research on cyberbullying dynamics.

Article Abstract

Introduction: Social media platforms such as Twitter and Weibo facilitate both positive and negative communication, including cyberbullying. Empirical evidence has revealed that cyberbullying increases when public crises occur, that such behavior is gendered, and that social media user account verification may deter it. However, the association of gender and verification status with cyberbullying is underexplored. This study aims to address this gap by examining how Weibo users' gender, verification status, and expression of affect and anger in posts influence cyberbullying attitudes. Specifically, it investigates how these factors differ between posts pro- and anti-cyberbullying of COVID-19 cases during the pandemic.

Methods: This study utilized social role theory, the Barlett and Gentile Cyberbullying Model, and general strain theory as theoretical frameworks. We applied text classification techniques to identify pro-cyberbullying and anti-cyberbullying posts on Weibo. Subsequently, we used a standardized mean difference method to compare the emotional content of these posts. Our analysis focused on the prevalence of affective and anger-related expressions, particularly examining variations across gender and verification status of the users.

Results: Our text classification identified distinct pro-cyberbullying and anti-cyberbullying posts. The standardized mean difference analysis revealed that pro-cyberbullying posts contained significantly more emotional content compared to anti-cyberbullying posts. Further, within the pro-cyberbullying category, posts by verified female users exhibited a higher frequency of anger-related words than those by other users.

Discussion: The findings from this study can enhance researchers' algorithms for identifying cyberbullying attitudes, refine the characterization of cyberbullying behavior using real-world social media data through the integration of the mentioned theories, and help government bodies improve their cyberbullying monitoring especially in the context of public health crises.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11210603PMC
http://dx.doi.org/10.3389/fpsyg.2024.1395668DOI Listing

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