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

Emotion-guided Cross-domain Fake News Detection using Adversarial Domain Adaptation