Mining Dual Emotion for Fake News Detection

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 <PRE_TAG>emotion</POST_TAG> and social <PRE_TAG>emotion</POST_TAG> (i.e., dual <PRE_TAG>emotion</POST_TAG>), and how the dual <PRE_TAG>emotion</POST_TAG> appears in fake news. In this paper, we verify that dual <PRE_TAG>emotion</POST_TAG> is distinctive between fake and real news and propose Dual Emotion Features to represent dual <PRE_TAG>emotion</POST_TAG> and the relationship between them for <PRE_TAG>fake news detection</POST_TAG>. 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 <PRE_TAG>emotional features</POST_TAG>; 2) can be well compatible with existing fake news detectors and effectively improve the performance of detecting fake news.