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

arXiv: https://arxiv.org/abs/2608.16323 · HTML: https://mlsanity.com/paper/2608.16323/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 38 of its 63 references that are in the map.
- First posted on arXiv 17 Aug 2026.

## Builds on (most cited first, 30 of 38)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014)
- [The COVID-19 Social Media Infodemic](https://mlsanity.com/paper/2003.05004/) (2020)
- [Uncovering Coordinated Networks on Social Media: Methods and Case Studies](https://mlsanity.com/paper/2001.05658/) (2020)
- [Identifying Coordinated Accounts on Social Media through Hidden Influence and Group Behaviours](https://mlsanity.com/paper/2008.11308/) (2020)
- [Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities](https://mlsanity.com/paper/2203.13883/) (2022)
- [Unmasking the Web of Deceit: Uncovering Coordinated Activity to Expose Information Operations on Twitter](https://mlsanity.com/paper/2310.09884/) (2023)
- [Moral Emotions Shape the Virality of COVID-19 Misinformation on Social Media](https://mlsanity.com/paper/2202.03590/) (2022)
- [The Spread of Propaganda by Coordinated Communities on Social Media](https://mlsanity.com/paper/2109.13046/) (2021)
- [Red Bots Do It Better: Comparative Analysis of Social Bot Partisan Behavior](https://mlsanity.com/paper/1902.02765/) (2019)
- ["This is Fake! Shared it by Mistake": Assessing the Intent of Fake News Spreaders](https://mlsanity.com/paper/2202.04752/) (2022)
- [Tracking Fringe and Coordinated Activity on Twitter Leading Up To the US Capitol Attack](https://mlsanity.com/paper/2302.04450/) (2023)
- [SCARLET: Explainable Attention based Graph Neural Network for Fake News spreader prediction](https://mlsanity.com/paper/2102.04627/) (2021)
- [VaccinEU: COVID-19 vaccine conversations on Twitter in French, German and Italian](https://mlsanity.com/paper/2201.06293/) (2022)
- [A Semi-supervised Fake News Detection using Sentiment Encoding and LSTM with Self-Attention](https://mlsanity.com/paper/2407.19332/) (2024)
- [The COVID-19 Infodemic: Twitter versus Facebook](https://mlsanity.com/paper/2012.09353/) (2020)
- [Profiling Fake News Spreaders on Social Media through Psychological and Motivational Factors](https://mlsanity.com/paper/2108.10942/) (2021)
- [Identifying and Characterizing Behavioral Classes of Radicalization within the QAnon Conspiracy on Twitter](https://mlsanity.com/paper/2209.09339/) (2022)
- [Exposing Cross-Platform Coordinated Inauthentic Activity in the Run-Up to the 2024 U.S. Election](https://mlsanity.com/paper/2410.22716/) (2024)
- [Quantifying the Vulnerabilities of the Online Public Square to Adversarial Manipulation Tactics](https://mlsanity.com/paper/1907.06130/) (2019)
- [Temporal Dynamics of Coordinated Online Behavior: Stability, Archetypes, and Influence](https://mlsanity.com/paper/2301.06774/) (2023)
- [Susceptibility to Unreliable Information Sources: Swift Adoption with Minimal Exposure](https://mlsanity.com/paper/2311.05724/) (2023)
- [Synthetic Politics: Prevalence, Spreaders, and Emotional Reception of AI-Generated Political Images on X](https://mlsanity.com/paper/2502.11248/) (2025)
- [Exposure to Social Engagement Metrics Increases Vulnerability to Misinformation](https://mlsanity.com/paper/2005.04682/) (2020)
- [One Year of COVID-19 Vaccine Misinformation on Twitter: Longitudinal Study](https://mlsanity.com/paper/2209.01675/) (2022)
- [TieFake: Title-Text Similarity and Emotion-Aware Fake News Detection](https://mlsanity.com/paper/2304.09421/) (2023)
- [The Susceptibility Paradox in Online Social Influence](https://mlsanity.com/paper/2406.11553/) (2024)
- [IOHunter: Graph Foundation Model to Uncover Online Information Operations](https://mlsanity.com/paper/2412.14663/) (2024)
- [Beyond Interaction Patterns: Assessing Claims of Coordinated Inter-State Information Operations on Twitter/X](https://mlsanity.com/paper/2502.17344/) (2025)
- [Coordinated Inauthentic Behavior on TikTok: Challenges and Opportunities for Detection in a Video-First Ecosystem](https://mlsanity.com/paper/2505.10867/) (2025)

## Abstract (arXiv)

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.