通过原型用户行为预测、评估和解释顶级错误信息传播者
Predicting, Evaluating, and Explaining Top Misinformation Spreaders via Archetypal User Behavior
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中文总结 AI 辅助
本研究提出结合放大器、超级传播者、协同账号三类用户原型的排名模型，引入时间动态与可解释AI技术，可识别顶级错误信息传播者，为内容审核提供工具。
中文摘要 AI 辅助
社交网络上错误信息的传播对在线社区乃至整个社会构成重大挑战。并非所有用户对此现象的贡献均等：少数高效个体可发挥巨大影响力，放大虚假叙事并造成重大社会危害。本文旨在通过主动干预减轻错误信息传播，依据与有害内容传播相关的关键行为指标识别并对用户排名。我们研究三种用户原型——放大器（amplifiers）、超级传播者（super-spreaders）和协同账号（coordinated accounts），每种原型在错误信息传播中具有 distinct 行为模式，且三者并非互斥，单个用户可能呈现多种原型特征。我们开发并评估若干与特定原型对齐的用户排名模型，发现超级传播者特征在最具影响力的错误信息传播者中始终主导排名前列，但排名靠后时，多种原型的相互作用更为显著。此外，我们证明时间动态对预测性能的关键作用，并引入方法通过最小化准确预测所需的观测窗口来减少数据需求。最后，我们证明可解释人工智能（XAI）技术的实用性与益处，将多种原型特征整合到统一模型中以增强可解释性，并深入洞察驱动错误信息传播的关键因素。我们的研究结果提供可操作的工具，用于识别潜在有害用户并指导内容审核策略，使平台能更有效地监控相关账号。
英文摘要
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.
发表机构
- SUPSI（瑞士应用科学大学）
- USC Information Sciences Institute（南加州大学信息科学研究所）
机构由 AI 辅助整理，请以论文原文为准。
