An Emotion-guided Approach to Domain Adaptive Fake News Detection using Adversarial Learning

2022/11/26 by Arkajyoti Chakraborty, Chakraborty, Arkajyoti, Inder Khatri +9
Computer Science · Social Sciences · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Misinformation and Its Impacts #Sentiment Analysis and Opinion Mining #Spam and Phishing Detection

paper · pdf · doi:10.48550/arxiv.2211.17108

openalex publication_date 2022/11/26 · openalex created_date 2022/12/12 · openalex updated_date 2026/07/28

Abstract

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.

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