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How Do Chinese People View Cyberbullying? A Text Analysis Based on Social Media

Author

Listed:
  • Shan Lu

    (Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Wuhan 430056, China
    Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan 430056, China
    School of Psychology, Central China Normal University, Wuhan 430056, China
    Department of Applied Psychology, School of Humanities and Social Sciences, Fuzhou University, Fuzhou 350108, China)

  • Lingbo Zhao

    (Department of Applied Psychology, School of Humanities and Social Sciences, Fuzhou University, Fuzhou 350108, China)

  • Lizu Lai

    (Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Wuhan 430056, China
    Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan 430056, China
    School of Psychology, Central China Normal University, Wuhan 430056, China)

  • Congrong Shi

    (Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Wuhan 430056, China
    Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan 430056, China
    School of Psychology, Central China Normal University, Wuhan 430056, China)

  • Wanyue Jiang

    (Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Wuhan 430056, China
    Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan 430056, China
    School of Psychology, Central China Normal University, Wuhan 430056, China)

Abstract

The rise of cyberbullying has been of great concern for the general public. This study aims to explore public attitudes towards cyberbullying on Chinese social media. Cognition and emotion are important components of attitude, and this study innovatively used text analysis to extract the cognition and emotion of the posts. We used a web crawler to collect 53,526 posts related to cyberbullying in Chinese on Sina Weibo in a month, where emotions were detected using the software “Text Mind”, a Chinese linguistic psychological text analysis system, and the content analysis was performed using the Latent Dirichlet Allocation topic model. Sentiment analysis showed the frequency of negative emotion words was the highest in the posts; the frequency of anger, anxiety, and sadness words decreased in turn. The topic model analysis identified three common topics about cyberbullying: critiques on cyberbullying and support for its victims, rational expressions of anger and celebrity worship, and calls for further control. In summary, this study quantitatively reveals the negative attitudes of the Chinese public toward cyberbullying and conveys specific public concerns via three common topics. This will help us to better understand the demands of the Chinese public so that targeted support can be proposed to curb cyberbullying.

Suggested Citation

  • Shan Lu & Lingbo Zhao & Lizu Lai & Congrong Shi & Wanyue Jiang, 2022. "How Do Chinese People View Cyberbullying? A Text Analysis Based on Social Media," IJERPH, MDPI, vol. 19(3), pages 1-13, February.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:3:p:1822-:d:742831
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    References listed on IDEAS

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    1. Brian O'Neill & Thuy Dinh, 2015. "Mobile Technologies and the Incidence of Cyberbullying in Seven European Countries: Findings from Net Children Go Mobile," Societies, MDPI, vol. 5(2), pages 1-15, April.
    2. Jinyu Huang & Zhaohao Zhong & Haoyuan Zhang & Liping Li, 2021. "Cyberbullying in Social Media and Online Games among Chinese College Students and Its Associated Factors," IJERPH, MDPI, vol. 18(9), pages 1-12, April.
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    Cited by:

    1. Leila Lefi & Sarra Sghaier, 2024. "Investigating the Dark Side of Social Media Marketing: Case of Cyberbullying on Micro Celebrities," International Review of Management and Marketing, Econjournals, vol. 14(2), pages 13-22, March.
    2. Li, Wanqi, 2024. "“Amusing ourselves to death”: Mechanisms in cyberbullying prompted by rumors and denigration amidst the COVID-19 pandemic in China," Technology in Society, Elsevier, vol. 76(C).

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