Predicting, Evaluating, and Explaining Top Misinformation Spreaders via Archetypal User Behavior
2026-08-17 • Social and Information Networks
Social and Information NetworksComputers and SocietyMachine Learning
AI summaryⓘ
The authors studied how misinformation spreads on social networks by focusing on different types of users: amplifiers, super-spreaders, and coordinated accounts. They created ranking models to identify which users are most influential in spreading false information, finding that super-spreader behavior is especially important. They also showed that looking at how user behavior changes over time helps predict misinformation spread better and developed methods needing less data for accurate predictions. Finally, they combined these user types into one explainable AI model to help platforms understand and manage harmful accounts more effectively.
misinformationsocial networkssuper-spreadersamplifierscoordinated accountsuser ranking modelstemporal dynamicsexplainable AIcontent moderationpredictive modeling
Authors
Enrico Verdolotti, Luca Luceri, Silvia Giordano
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.