Emotion-guided Cross-domain Fake News Detection using Adversarial Domain Adaptation
Arjun Choudhry, Inder Khatri, Arkajyoti Chakraborty, Dinesh Vishwakarma, Mukesh Prasad
Abstract
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.- Anthology ID:
- 2022.icon-main.10
- Volume:
- Proceedings of the 19th International Conference on Natural Language Processing (ICON)
- Month:
- December
- Year:
- 2022
- Address:
- New Delhi, India
- Editors:
- Md. Shad Akhtar, Tanmoy Chakraborty
- Venue:
- ICON
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 75–79
- Language:
- URL:
- https://aclanthology.org/2022.icon-main.10
- DOI:
- Cite (ACL):
- Arjun Choudhry, Inder Khatri, Arkajyoti Chakraborty, Dinesh Vishwakarma, and Mukesh Prasad. 2022. Emotion-guided Cross-domain Fake News Detection using Adversarial Domain Adaptation. In Proceedings of the 19th International Conference on Natural Language Processing (ICON), pages 75–79, New Delhi, India. Association for Computational Linguistics.
- Cite (Informal):
- Emotion-guided Cross-domain Fake News Detection using Adversarial Domain Adaptation (Choudhry et al., ICON 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2022.icon-main.10.pdf