AAAI 2023technical5 citations
An Emotion-Guided Approach to Domain Adaptive Fake News Detection Using Adversarial Learning (Student Abstract)
Arkajyoti Chakraborty, Inder Khatri, Arjun Choudhry, Pankaj Gupta, Dinesh Kumar Vishwakarma, Mukesh Prasad
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.
BibTeX
@article{Chakraborty_Khatri_Choudhry_Gupta_Vishwakarma_Prasad_2024, title={An Emotion-Guided Approach to Domain Adaptive Fake News Detection Using Adversarial Learning (Student Abstract)}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26949}, DOI={10.1609/aaai.v37i13.26949}, abstractNote={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.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Chakraborty, Arkajyoti and Khatri, Inder and Choudhry, Arjun and Gupta, Pankaj and Vishwakarma, Dinesh Kumar and Prasad, Mukesh}, year={2024}, month={Jul.}, pages={16178-16179} }