The Battlefront of Combating Misinformation and Coping with Media Bias

Yi Fung, Kung-Hsiang Huang, Preslav Nakov, Heng Ji


Abstract
Misinformation is a pressing issue in modern society. It arouses a mixture of anger, distrust, confusion, and anxiety that cause damage on our daily life judgments and public policy decisions. While recent studies have explored various fake news detection and media bias detection techniques in attempts to tackle the problem, there remain many ongoing challenges yet to be addressed, as can be witnessed from the plethora of untrue and harmful content present during the COVID-19 pandemic and the international crises of late. In this tutorial, we provide researchers and practitioners with a systematic overview of the frontier in fighting misinformation. Specifically, we dive into the important research questions of how to (i) develop a robust fake news detection system, which not only fact-check information pieces provable by background knowledge but also reason about the consistency and the reliability of subtle details for emerging events; (ii) uncover the bias and agenda of news sources to better characterize misinformation; as well as (iii) correct false information and mitigate news bias, while allowing diverse opinions to be expressed. Moreover, we discuss the remaining challenges, future research directions, and exciting opportunities to help make this world a better place, with safer and more harmonic information sharing.
Anthology ID:
2022.aacl-tutorials.5
Volume:
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: Tutorial Abstracts
Month:
November
Year:
2022
Address:
Taipei
Venues:
AACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
28–34
Language:
URL:
https://aclanthology.org/2022.aacl-tutorials.5
DOI:
Bibkey:
Cite (ACL):
Yi Fung, Kung-Hsiang Huang, Preslav Nakov, and Heng Ji. 2022. The Battlefront of Combating Misinformation and Coping with Media Bias. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: Tutorial Abstracts, pages 28–34, Taipei. Association for Computational Linguistics.
Cite (Informal):
The Battlefront of Combating Misinformation and Coping with Media Bias (Fung et al., AACL-IJCNLP 2022)
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PDF:
https://preview.aclanthology.org/auto-file-uploads/2022.aacl-tutorials.5.pdf