An Emotion-Guided Approach to Domain Adaptive Fake News Detection Using Adversarial Learning (Student Abstract)

Recent works on fake news detection have shown the efficacy of using emotions as a feature for improved performance. However, cross-domain impact emotion-guided features still remains an open problem. In this work, we propose emotion-guided, domain-adaptive, multi-task approach detection, proving models in settings various datasets.