  Recent works on fake news detection have shown the efficacy of using emotions
as a feature or emotions-based features for improved performance. However, the
impact of these emotion-guided features for fake news detection in cross-domain
settings, where we face the problem of domain shift, is still largely
unexplored. In this work, we evaluate the impact of emotion-guided features for
cross-domain fake news detection, and further propose an emotion-guided,
domain-adaptive approach using adversarial learning. We prove the efficacy of
emotion-guided models in cross-domain settings for various combinations of
source and target datasets from FakeNewsAMT, Celeb, Politifact and Gossipcop
datasets.
