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

Authors

  • Arkajyoti Chakraborty Biometric Research Laboratory, Delhi Technological University
  • Inder Khatri Biometric Research Laboratory, Delhi Technological University
  • Arjun Choudhry Biometric Research Laboratory, Delhi Technological University
  • Pankaj Gupta Biometric Research Laboratory, Delhi Technological University
  • Dinesh Kumar Vishwakarma Biometric Research Laboratory, Delhi Technological University
  • Mukesh Prasad School of Computer Science, University of Technology Sydney

DOI:

https://doi.org/10.1609/aaai.v37i13.26949

Keywords:

Fake News Detection, Domain Adaptation, Adversarial Learning, Emotion Classification, Text Classification, Cross-domain Analysis

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.
AAAI-23 Proceedings Cover

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Published

2023-09-06

How to Cite

Chakraborty, A., Khatri, I., Choudhry, A., Gupta, P., Vishwakarma, D. K., & Prasad, M. (2023). An Emotion-Guided Approach to Domain Adaptive Fake News Detection Using Adversarial Learning (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16178-16179. https://doi.org/10.1609/aaai.v37i13.26949

Issue

Vol. 37 No. 13: AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations

Section

AAAI Student Abstract and Poster Program