ML Anthology

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
AAAI 2023 pp. 16178-16179
doi:10.1609/AAAI.V37I13.26949 /aaai/2023/chakraborty2023aaai-emotion/

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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Text

Chakraborty et al. "An Emotion-Guided Approach to Domain Adaptive Fake News Detection Using Adversarial Learning (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.26949

Markdown

[Chakraborty et al. "An Emotion-Guided Approach to Domain Adaptive Fake News Detection Using Adversarial Learning (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/chakraborty2023aaai-emotion/) doi:10.1609/AAAI.V37I13.26949

BibTeX

@inproceedings{chakraborty2023aaai-emotion,
  title     = {{An Emotion-Guided Approach to Domain Adaptive Fake News Detection Using Adversarial Learning (Student Abstract)}},
  author    = {Chakraborty, Arkajyoti and Khatri, Inder and Choudhry, Arjun and Gupta, Pankaj and Vishwakarma, Dinesh Kumar and Prasad, Mukesh},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16178-16179},
  doi       = {10.1609/AAAI.V37I13.26949},
  url       = {https://mlanthology.org/aaai/2023/chakraborty2023aaai-emotion/}
}