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Social Bots’ Involvement in the COVID-19 Vaccine Discussions on Twitter

Author

Listed:
  • Menghan Zhang

    (Centre for Chinese Urbanization Studies of Soochow University & Collaborative Innovation Center for New Urbanization and Social Governance of Universities, Suzhou 215006, China
    School of Communication, Soochow University, Suzhou 215123, China)

  • Xue Qi

    (School of Communication, Soochow University, Suzhou 215123, China)

  • Ze Chen

    (School of Communication, Soochow University, Suzhou 215123, China)

  • Jun Liu

    (Department of Communication, University of Copenhagen, DK-2300 Copenhagen, Denmark)

Abstract

During the COVID-19 pandemic, social media served as an important channel for the public to obtain health information and disseminate opinions when offline communication was severely hindered. Yet the emergence of social bots influencing social media conversations about public health threats will require researchers and practitioners to develop new communication strategies considering their influence. So far, little is known as to what extent social bots have been involved in COVID-19 vaccine-related discussions and debates on social media. This work selected a period of nearly 9 months after the approval of the first COVID-19 vaccines to detect social bots and performed high-frequency word analysis for both social bot-generated and human-generated tweets, thus working out the extent to which social bots participated in the discussion on the COVID-19 vaccine on Twitter and their participation features. Then, a textual analysis was performed on the content of tweets. The findings revealed that 8.87% of the users were social bots, with 11% of tweets in the corpus. Besides, social bots remained active over three periods. High-frequency words in the discussions of social bots and human users on vaccine topics were similar within the three peaks of discourse.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:3:p:1651-:d:739793
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    1. Yuye Zhou & Jiangang Xu & Maosen Yin & Jun Zeng & Haolin Ming & Yiwen Wang, 2022. "Spatial-Temporal Pattern Evolution of Public Sentiment Responses to the COVID-19 Pandemic in Small Cities of China: A Case Study Based on Social Media Data Analysis," IJERPH, MDPI, vol. 19(18), pages 1-18, September.

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