An Emotion-guided Approach to Domain Adaptive Fake News Detection using Adversarial Learning
This work addresses the challenge of detecting fake news across different domains, which is an incremental improvement for social media and information verification systems.
The paper tackled the problem of cross-domain fake news detection by proposing an emotion-guided, domain-adaptive, multi-task approach, demonstrating its efficacy across various datasets.
Recent works on fake news detection have shown the efficacy of using emotions as a feature for improved performance. However, the cross-domain impact of emotion-guided features for fake news detection still remains an open problem. In this work, we propose an emotion-guided, domain-adaptive, multi-task approach for cross-domain fake news detection, proving the efficacy of emotion-guided models in cross-domain settings for various datasets.