用众包智慧识别网络谣言，发现普通人判断常准且可解释。
In Crowd Veritas: Leveraging Human Intelligence To Fight Misinformation
- 通过大规模众包实验收集非专家判断，分析影响真实性的关键因素。
- 非专家判断与专家结论高度一致，尤其在考虑时间与经验后。
- 提出联合预测与解释模型，适合开发透明可信的反假系统。
在线虚假信息的传播对民主社会构成严重威胁。传统上，专家通过调查核实信息真实性，但网络内容体量大、更新快，难以规模化应对。众包提供替代方案，利用非专家判断，但存在偏见、准确性和可解释性问题。本论文研究如何利用人类智能评估网络信息的真实性，聚焦三个方向：虚假信息评估、认知偏见、自动化事实核查系统。通过大规模众包实验与统计建模，识别影响人类判断的关键因素，并提出一个联合预测与解释真实性的模型。研究发现，非专家判断在考虑时间与经验后，往往与专家评估一致。深化对人类判断与偏见的理解，有助于构建更透明、可信、可解释的反虚假信息系统。
原文摘要 · Abstract (English)
The spread of online misinformation poses serious threats to democratic societies. Traditionally, expert fact-checkers verify the truthfulness of information through investigative processes. However, the volume and immediacy of online content present major scalability challenges. Crowdsourcing offers a promising alternative by leveraging non-expert judgments, but it introduces concerns about bias, accuracy, and interpretability. This thesis investigates how human intelligence can be harnessed to assess the truthfulness of online information, focusing on three areas: misinformation assessment, cognitive biases, and automated fact-checking systems. Through large-scale crowdsourcing experiments and statistical modeling, it identifies key factors influencing human judgments and introduces a model for the joint prediction and explanation of truthfulness. The findings show that non-expert judgments often align with expert assessments, particularly when factors such as timing and experience are considered. By deepening our understanding of human judgment and bias in truthfulness assessment, this thesis contributes to the development of more transparent, trustworthy, and interpretable systems for combating misinformation.
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