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Comparative analysis of social bots and humans during the COVID-19 pandemic

Author

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  • Ho-Chun Herbert Chang

    (University of Southern California
    Information Science Institute)

  • Emilio Ferrara

    (Information Science Institute
    USC)

Abstract

Using more than 4 billion tweets and labels on more than 5 million users, this paper compares the behavior of humans and bots politically and semantically during the pandemic. Results reveal liberal bots are more central than humans in general, but less important than institutional humans as the elite circle grows smaller. Conservative bots are surprisingly absent when compared to prior work on political discourse, but are better than liberal bots at eliciting replies from humans, which suggest they may be perceived as human more frequently. In terms of topic and framing, conservative humans and bots disproportionately tweet about the Bill Gates and bio-weapons conspiracy, whereas the 5G conspiracy is bipartisan. Conservative humans selectively ignore mask-wearing and we observe prevalent out-group tweeting when discussing policy. We discuss and contrast how humans appear more centralized in health-related discourse as compared to political events, which suggests the importance of credibility and authenticity for public health in online information diffusion.

Suggested Citation

  • Ho-Chun Herbert Chang & Emilio Ferrara, 2022. "Comparative analysis of social bots and humans during the COVID-19 pandemic," Journal of Computational Social Science, Springer, vol. 5(2), pages 1409-1425, November.
    • Handle: RePEc:spr:jcsosc:v:5:y:2022:i:2:d:10.1007_s42001-022-00173-9
    DOI: 10.1007/s42001-022-00173-9
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    References listed on IDEAS

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    1. Druckman, James N. & Peterson, Erik & Slothuus, Rune, 2013. "How Elite Partisan Polarization Affects Public Opinion Formation," American Political Science Review, Cambridge University Press, vol. 107(1), pages 57-79, February.
    2. Sandra González-Bailón & Manlio De Domenico, 2021. "Bots are less central than verified accounts during contentious political events," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(11), pages 2013443118-, March.
    3. Alexandre Bovet & Hernán A. Makse, 2019. "Influence of fake news in Twitter during the 2016 US presidential election," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    4. Bjarke Mønsted & Piotr Sapieżyński & Emilio Ferrara & Sune Lehmann, 2017. "Evidence of complex contagion of information in social media: An experiment using Twitter bots," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-12, September.
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    Cited by:

    1. 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.
    2. Ali Unlu & Sophie Truong & Nitin Sawhney & Jonas Sivelä & Tuukka Tammi, 2024. "Long-term assessment of social amplification of risk during COVID-19: challenges to public health agencies amid misinformation and vaccine stance," Journal of Computational Social Science, Springer, vol. 7(1), pages 809-836, April.

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