Research on the Evolution Mechanism of Weibo Online Public Opinion Emotions — A Computational Communication Analysis Based on the 2024 Juvenile Crime Incident in Handan, Hebei Province

Wanyi Cao

doi:10.63313/JCSFT.9033

Authors

Wanyi Cao School of Publishing, University of Shanghai for Science and Technology, Shanghai 200093, China Author

DOI:

https://doi.org/10.63313/JCSFT.9033

Keywords:

Computational Communication, Emotional Evolution, Juvenile Crime, Technological Stigma, Public Opinion Guidance

Abstract

This study takes the extreme juvenile crime incident where three junior high school students killed their classmate in Handan, Hebei Province in 2024 as the research object. Employing computational communication research methods, it conducts sentiment analysis, topic mining, and social network analysis on 447,918 relevant original Weibo data. The research aims to reveal the evolutionary path of Weibo online public opinion emotions related to cross-cutting issues of technology and morality in the context of sudden social events. The results indicate that the public opinion emotions triggered by the incident exhibit a three-stage evolutionary characteristic of "shock and anger — in-depth reflection — rational attention". The technical issue of AI campus monitoring was reframed by opinion leaders during the incident investigation stage and further deeply bound to public emotions during the trial stage. Official media and opinion leaders play key roles as "emotional brakes" and "issue reframes" respectively in the public opinion field, jointly influencing the emotional evolution process. This study not only provides new empirical evidence for understanding the media generation mechanism of technological stigmatization but also offers practically significant strategic suggestions for the precise governance and guidance of public opinion related to juvenile crimes, as well as the improvement of the juvenile crime prevention system.

References

[1] Yu, G. M., & Li, F. (2025). Computational Communication: Current Situation, Problems and Construction of Disciplinary System. Journalists' Friend, (06), 10-17.

[2] Wang, C. J. (2025). The Methods, Practices and Theoretical Logic of Computational Com-munication: A Review of Computational Social Science Research in Domestic Journalism and Communication Studies (2014-2024). Educational Media Research, 1-13. Retrieved from [2025-08-26].

[3] Ma, H. L. (2025). A "Reciprocal" Study of the AI Agenda on Social Media — A Computation-al Communication Analysis Based on 2024 Weibo Data. New Media Research, 11(12), 13-19.

[4] Li, H., Wu, Y., Yuan, F. Y., et al. (2025). The Innovation Path of Computational Communica-tion: A Systematic Analysis of Theoretical Frameworks, Research Methods and Application Practices — A Review of Computational Communication Research in 2024. Educational Media Research, (01), 32-39.

[5] Sun, S. J., & Liu, Z. Y. (2024). Issue Visibility and Gatekeeping Actors of Weibo Hot Searches — A Computational Communication Analysis Based on Public Opinion Events. Contempo-rary Communication, (05), 30-36+43.

[6] Shou, Y. J. (2024). Emotional Analysis of Users' Media Usage from the Perspective of Hu-man-Computer Communication — A Computational Communication Research Based on Weibo Users' Posts on ChatGPT Usage. Audio-Visual, (09), 139-144.

[7] Lin, Y. Y., & Nong, B. L. (2025). Social Media Public Opinion Mining of Generative Artificial Intelligence — A Computational Communication Analysis Based on ChatGPT and Sora Weibo Topics. Journal of Nanjing University of Posts and Telecommunications (Social Science Edition), 1-14. Retrieved from [2025-08-26].

[8] Liu, C. X., Deng, Y., & Zhang, Z. (2025). The Mediated Construction and Image Clarification of Small Towns — A Computational Communication Analysis Based on Weibo Texts. Glob-al Media Journal, 12(02), 113-131.

[9] Zhang, P. J., & Song, L. (2012). A Review of Topic Modeling Methods for Weibo Texts Based on LDA. Library and Information Service, 56(24), 120-126.