Predicting, Evaluating, and Explaining Top Misinformation Spreaders via Archetypal User Behavior

Abstract:The spread of misinformation on social networks poses a significant challenge to online communities and society at large. Not all users contribute equally to this phenomenon: a small number of highly effective individuals can exert outsized influence, amplifying false narratives and contributing to significant societal harm. This paper seeks to mitigate the spread of misinformation by enabling proactive interventions, identifying and ranking users according to key behavioral indicators associated with harmful content dissemination. We examine three user archetypes -- amplifiers, super-spreaders, and coordinated accounts -- each characterized by distinct behavioral patterns in the dissemination of misinformation. These are not mutually exclusive, and individual users may exhibit characteristics of multiple archetypes. We develop and evaluate several user ranking models, each aligned with a specific archetype, and find that super-spreader traits consistently dominate the top ranks among the most influential misinformation spreaders. As we move down the ranking, however, the interplay of multiple archetypes becomes more prominent. Additionally, we demonstrate the critical role of temporal dynamics in predictive performance, and introduce methods that reduce data requirements by minimizing the observation window needed for accurate forecasting. Finally, we demonstrate the utility and benefits of explainable AI (XAI) techniques, integrating multiple archetypal traits into a unified model to enhance interpretability and offer deeper insight into the key factors driving misinformation propagation. Our findings provide actionable tools for identifying potentially harmful users and guiding content moderation strategies, enabling platforms to monitor accounts of concern more effectively.

48 pages. Published version: Online Social Networks and Media 50 (2025) 100336

Social and Information Networks (cs.SI); Computers and Society (cs.CY); Machine Learning (cs.LG)

H.2.8; I.2.6; J.4

arXiv:2608.16323 [cs.SI]

(or arXiv:2608.16323v1 [cs.SI] for this version)

https://doi.org/10.48550/arXiv.2608.16323 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1016/j.osnem.2025.100336 Focus to learn more DOI(s) linking to related resources

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