  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.
