An Emotion-Based Multi-Task Approach to Fake News Detection (Student Abstract)

Arjun Choudhry, Inder Khatri, Minni Jain

AAAI 2022 pp. 12929-12930

doi:10.1609/AAAI.V36I11.21601 /aaai/2022/choudhry2022aaai-emotion/

Abstract

Social media, blogs, and online articles are instant sources of news for internet users globally. But due to their unmoderated nature, a significant percentage of these texts are fake news or rumors. Their deceptive nature and ability to propagate instantly can have an adverse effect on society. In this work, we hypothesize that legitimacy of news has a correlation with its emotion, and propose a multi-task framework predicting both the emotion and legitimacy of news. Experimental results verify that our multi-task models outperform their single-task counterparts in terms of accuracy.

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Cite

Text

Choudhry et al. "An Emotion-Based Multi-Task Approach to Fake News Detection (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21601

Markdown

[Choudhry et al. "An Emotion-Based Multi-Task Approach to Fake News Detection (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/choudhry2022aaai-emotion/) doi:10.1609/AAAI.V36I11.21601

BibTeX

@inproceedings{choudhry2022aaai-emotion,
  title     = {{An Emotion-Based Multi-Task Approach to Fake News Detection (Student Abstract)}},
  author    = {Choudhry, Arjun and Khatri, Inder and Jain, Minni},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {12929-12930},
  doi       = {10.1609/AAAI.V36I11.21601},
  url       = {https://mlanthology.org/aaai/2022/choudhry2022aaai-emotion/}
}