Predicting, evaluating, and explaining top misinformation spreaders via archetypal user behavior

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. • User behavioral archetypes: amplifiers , super-spreaders , coordinated accounts . • Methods combining archetypes to rank users by risk of misinformation spreading. • Explainable AI highlights behavioral traits of major misinformation propagators.

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Publication Details

Journal
Online Social Networks and Media
Published
2025-10-04
DOI
https://doi.org/10.1016/j.osnem.2025.100336
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
0.00
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article

Predicting, evaluating, and explaining top misinformation spreaders via archetypal user behavior

Silvia Giordano, Luca Luceri, Enrico Verdolotti
Online Social Networks and Media
Misinformation and Its Impacts
article

Predicting, evaluating, and explaining top misinformation spreaders via archetypal user behavior

Silvia Giordano, Luca Luceri, Enrico Verdolotti
article en

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. • User behavioral archetypes: amplifiers , super-spreaders , coordinated accounts . • Methods combining archetypes to rank users by risk of misinformation spreading. • Explainable AI highlights behavioral traits of major misinformation propagators.

Online Social Networks and MediaVol. 50
University of Applied Sciences and Arts of Southern Switzerland (CH), Dalle Molle Institute for Artificial Intelligence Research (CH), Marina Del Rey Hospital (US)
Openalex Percentile: Top 64%
Misinformation and Its Impacts
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