Mining Dual Emotion for Fake News Detection

By Xueyao Zhang, Juan Cao, Xirong Li, Qiang Sheng, Lei Zhong, Kai Shu. The Web Conference, 2019

348 citations recorded by the source corpus.

Emotion plays an important role in detecting fake news online. When leveraging emotional signals, the existing methods focus on exploiting the emotions of news contents that conveyed by the publishers (i.e., publisher emotion). However, fake news often evokes high-arousal or activating emotions of people, so the emotions of news comments aroused in the crowd (i.e., social emotion) should not be ignored. Furthermore, it remains to be explored whether there exists a relationship between publisher emotion and social emotion (i.e., dual emotion), and how the dual emotion appears in fake news. In this paper, we verify that dual emotion is distinctive between fake and real news and propose Dual Emotion Features to represent dual emotion and the relationship between them for fake news detection. Further, we exhibit that our proposed features can be easily plugged into existing fake news detectors as an enhancement. Extensive experiments on three real-world datasets (one in English and the others in Chinese) show that our proposed feature set: 1) outperforms the state-of-the-art task-related emotional features; 2) can be well compatible with existing fake news detectors and effectively improve the performance of detecting fake news.1 2

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/f9e5b8057aefd59ce74a9ba1c57b88ee0b198917

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Citations in the corpus

A Survey on Automated Fact-Checking (2021, methodology): “Recent work explored fusing more domain-specific features into neural models (Zhang et al., 2021).”
Cross-modal Ambiguity Learning for Multimodal Fake News Detection (2022, background): “While people enjoy the convenience of online social media, the lack of systematic efforts to verify the credibility of online posts has led to the wide and fast spread of fake news across social platforms [19, 38, 40].”