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

• URL: http://arxiv.org/abs/2211.13718v1
• Date: Thu, 24 Nov 2022 17:11:56 GMT
• Title: Emotion-guided Cross-domain Fake News Detection using Adversarial Domain Adaptation
• Authors: Arjun Choudhry, Inder Khatri, Arkajyoti Chakraborty, Dinesh Kumar Vishwakarma, Mukesh Prasad
• Abstract summary: We evaluate the impact of emotion-guided features for cross-domain fake news detection. We propose an emotion-guided, domain-adaptive approach using adversarial learning.
• Score: 14.428215696969872
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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