发表机构
Syracuse University; University of California, Santa Barbara（雪城大学; 加利福尼亚大学圣塔芭芭拉分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一个基于理论的跨学科计算框架，将假新闻理论转化为可测量特征，用于自动检测与解释，实验表明理论特征具有预测性并支持可解释诊断。
AI 中文摘要
虚假信息研究已催生出日益精确的自动假新闻检测器，但许多系统仍难以解释，且与既有的说服、可信度和人类判断理论联系薄弱。本文中，我们开发了一个基于理论的计算框架，通过统计技术和大语言模型，将跨学科的假新闻理论转化为可测量的特征，用于自动检测和解释。为此，我们对社会科学、心理学、经济学等学科的理论进行了结构化的跨学科综述，揭示假新闻如何说服和传播，从而为计算建模奠定广泛的理论基础。基准数据集上的实验表明，理论衍生特征具有预测性，并提供可解释的、参照理论的诊断信号。多特征模型通常优于单一特征，尽管最强的小型特征组合之间的增益有限。我们的工作凸显了跨学科视角在构建稳健且可解释的假新闻检测系统中的价值，为以人为中心的反虚假信息方法奠定了基础。
英文摘要
Disinformation research has produced increasingly accurate automated fake-news detectors, but many systems remain difficult to interpret and are weakly connected to established theories of persuasion, credibility, and human judgment. In this paper, we develop a theory-informed computational framework that translates cross-disciplinary theories of fake news into measurable features for automated detection and explanation through statistical techniques and large language models. To that end, we conduct a structured cross-disciplinary review of theories from social sciences, psychology, economics, among other disciplines that reveal how fake news persuades and spreads, thereby establishing a broad theoretical foundation for computational modeling. Experiments on benchmark datasets show that theory-derived features are predictive and provide interpretable, theory-referenced diagnostic signals. Multi-feature models generally outperform individual features, although gains among the strongest small feature combinations are modest. Our work highlights the value of interdisciplinary perspectives in building robust and interpretable fake news detection systems, advancing the foundation for human-centered approaches in combating disinformation.
