发表机构
University of Zurich; Boğaziçi University（苏黎世大学; 海峡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
FakeSpotter通过测量文本结构指纹而非裁决真伪，在764条文本上以宏F1约0.79实现可解释的病毒式虚假信息早期检测。
AI 中文摘要
虚假信息检测工具通常依赖于二元真/假分类或基于历史示例训练的模型，这限制了它们在出现新颖误导性叙事时的实用性。在此，我们提出FakeSpotter，一种内容与策略无关的工具，旨在通过测量虚假信息的结构指纹而非直接裁决真实性，来估计文本内容的病毒式虚假信息风险。FakeSpotter在语言、叙事、逻辑和批判性思维维度上实施了一个理论驱动的框架，使用重复的LLM评估以及针对短文本和长文本的领域特定逻辑回归分类器。在一个包含来自社交媒体和FakeNewsNet的764条文本的标注语料库中，FakeSpotter在保留测试集上对短文本实现了0.788的宏F1分数，对长文本实现了0.793的宏F1分数。FakeSpotter的解释层通过基于特征的分数、信号一致性和谨慎指数提供可解释的输出，并可用于社会倾听。这些发现表明，识别虚假信息的结构指纹可以支持对潜在病毒式虚假信息进行早期、可解释且人工监督的评估。
英文摘要
Misinformation detection tools often rely on binary true and false classifications or models trained on historical examples, limiting their usefulness when novel misleading narratives emerge. Here, we present FakeSpotter, a content- and strategy-agnostic tool designed to estimate the viral misinformation risk of textual content by measuring structural fingerprints of misinformation rather than directly adjudicating truthfulness. FakeSpotter operationalizes a theory-driven framework across linguistic, narrative, logical, and critical-thinking dimensions, using repeated LLM assessments and domain-specific logistic regression classifiers for short and long texts. In a labelled corpus of 764 texts from social media and FakeNewsNet, FakeSpotter achieved macro F1 scores of 0.788 for short texts and 0.793 for long texts on a held-out test set. FakeSpotter's interpretive layer provides explainable outputs through feature-based scores, signal agreement, and a caution index, and can be used for social listening. These findings suggest that identifying the structural fingerprints of misinformation can support early, explainable, and human-supervised assessment of potentially viral misinformation.
