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Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines

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  • Joshua Uyheng

    (Carnegie Mellon University)

  • Kathleen M. Carley

    (Carnegie Mellon University)

Abstract

Online hate speech represents a serious problem exacerbated by the ongoing COVID-19 pandemic. Although often anchored in real-world social divisions, hate speech in cyberspace may also be fueled inorganically by inauthentic actors like social bots. This work presents and employs a methodological pipeline for assessing the links between hate speech and bot-driven activity through the lens of social cybersecurity. Using a combination of machine learning and network science tools, we empirically characterize Twitter conversations about the pandemic in the United States and the Philippines. Our integrated analysis reveals idiosyncratic relationships between bots and hate speech across datasets, highlighting different network dynamics of racially charged toxicity in the US and political conflicts in the Philippines. Most crucially, we discover that bot activity is linked to higher hate in both countries, especially in communities which are denser and more isolated from others. We discuss several insights for probing issues of online hate speech and coordinated disinformation, especially through a global approach to computational social science.

Suggested Citation

  • Joshua Uyheng & Kathleen M. Carley, 2020. "Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines," Journal of Computational Social Science, Springer, vol. 3(2), pages 445-468, November.
    • Handle: RePEc:spr:jcsosc:v:3:y:2020:i:2:d:10.1007_s42001-020-00087-4
    DOI: 10.1007/s42001-020-00087-4
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    Cited by:

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    5. J. Franceschi & L. Pareschi & M. Zanella, 2022. "From agent-based models to the macroscopic description of fake-news spread: the role of competence in data-driven applications," Partial Differential Equations and Applications, Springer, vol. 3(6), pages 1-26, December.
    6. Anna Ruelens, 2022. "Analyzing user-generated content using natural language processing: a case study of public satisfaction with healthcare systems," Journal of Computational Social Science, Springer, vol. 5(1), pages 731-749, May.
    7. 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.
    8. Wentao Xu & Kazutoshi Sasahara, 2022. "Characterizing the roles of bots on Twitter during the COVID-19 infodemic," Journal of Computational Social Science, Springer, vol. 5(1), pages 591-609, May.
    9. Francesca Bolla Tripodi, 2022. "ReOpen demands as public health threat: a sociotechnical framework for understanding the stickiness of misinformation," Computational and Mathematical Organization Theory, Springer, vol. 28(4), pages 321-334, December.
    10. Yaming Zhang & Wenjie Song & Jiang Shao & Majed Abbas & Jiaqi Zhang & Yaya H. Koura & Yanyuan Su, 2023. "Social Bots’ Role in the COVID-19 Pandemic Discussion on Twitter," IJERPH, MDPI, vol. 20(4), pages 1-21, February.

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