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Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter

Author

Listed:
  • Wen Shi

    (Department of Earth System Science, Tsinghua University, Beijing 100084, China)

  • Diyi Liu

    (School of Journalism and Communication, Renmin University of China, Beijing 100084, China)

  • Jing Yang

    (School of Journalism and Communication, Tsinghua University, Beijing 100084, China)

  • Jing Zhang

    (School of Journalism and Communication, Tsinghua University, Beijing 100084, China)

  • Sanmei Wen

    (Center for International Communication Studies, Tsinghua University, Beijing 100084, China)

  • Jing Su

    (School of Humanities, Tsinghua University, Beijing 100084, China)

Abstract

During the COVID-19 pandemic, when individuals were confronted with social distancing, social media served as a significant platform for expressing feelings and seeking emotional support. However, a group of automated actors known as social bots have been found to coexist with human users in discussions regarding the coronavirus crisis, which may pose threats to public health. To figure out how these actors distorted public opinion and sentiment expressions in the outbreak, this study selected three critical timepoints in the development of the pandemic and conducted a topic-based sentiment analysis for bot-generated and human-generated tweets. The findings show that suspected social bots contributed to as much as 9.27% of COVID-19 discussions on Twitter. Social bots and humans shared a similar trend on sentiment polarity—positive or negative—for almost all topics. For the most negative topics, social bots were even more negative than humans. Their sentiment expressions were weaker than those of humans for most topics, except for COVID-19 in the US and the healthcare system. In most cases, social bots were more likely to actively amplify humans’ emotions, rather than to trigger humans’ amplification. In discussions of COVID-19 in the US, social bots managed to trigger bot-to-human anger transmission. Although these automated accounts expressed more sadness towards health risks, they failed to pass sadness to humans.

Suggested Citation

  • Wen Shi & Diyi Liu & Jing Yang & Jing Zhang & Sanmei Wen & Jing Su, 2020. "Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter," IJERPH, MDPI, vol. 17(22), pages 1-18, November.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:22:p:8701-:d:449714
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    References listed on IDEAS

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    Cited by:

    1. Menghan Zhang & Xue Qi & Ze Chen & Jun Liu, 2022. "Social Bots’ Involvement in the COVID-19 Vaccine Discussions on Twitter," IJERPH, MDPI, vol. 19(3), pages 1-14, January.
    2. Zixuan Weng & Aijun Lin, 2022. "Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(24), pages 1-17, December.
    3. Sumayh S. Aljameel & Dina A. Alabbad & Norah A. Alzahrani & Shouq M. Alqarni & Fatimah A. Alamoudi & Lana M. Babili & Somiah K. Aljaafary & Fatima M. Alshamrani, 2020. "A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia," IJERPH, MDPI, vol. 18(1), pages 1-12, December.
    4. Lin, Trisha T. C., 2022. "Investigating the relationship of disguised socialbots and disinformation threat in Taiwan," 31st European Regional ITS Conference, Gothenburg 2022: Reining in Digital Platforms? Challenging monopolies, promoting competition and developing regulatory regimes 265654, International Telecommunications Society (ITS).

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