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Exposing the behavior of rumour spreaders on Twitter: targeted versus non-targeted rumour

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  • Yu-Chen Yang

    (University of Tartu, Institute of Computer Science)

  • Shakshi Sharma

    (Bennett University, School of Artificial Intelligence)

  • Rajesh Sharma

    (Plaksha University, School of AI and CS)

Abstract

Social media’s rise has facilitated the spread of false news, which frequently targets particular groups (such as Muslims and Black people) and spreads false accusations against them. Comprehending these objectives is essential for improving automated systems for detecting false information and developing plans to lessen the adverse effects of rumours. To find posts directed toward particular groups, we examined eight events of the PHEME-9 rumour dataset in this study. According to our exploratory analysis, targeted tweets frequently center on social issues and elicit more unfavorable responses than non-targeted ones. We also looked at the dynamics of the echo chamber among the users. This research sheds light on the language patterns of targeted tweets and suggests possible countermeasures. By combining linguistic, user, and sentiment perspectives-complementary layers of the same rumour process-we provide a comprehensive understanding of how targeted rumours differ from non-targeted ones. Through Google Drive (link will be shared upon request), the expanded rumour collection with targeting labels is accessible to the public.

Suggested Citation

  • Yu-Chen Yang & Shakshi Sharma & Rajesh Sharma, 2026. "Exposing the behavior of rumour spreaders on Twitter: targeted versus non-targeted rumour," Journal of Computational Social Science, Springer, vol. 9(1), pages 1-46, February.
    • Handle: RePEc:spr:jcsosc:v:9:y:2026:i:1:d:10.1007_s42001-025-00443-2
    DOI: 10.1007/s42001-025-00443-2
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