On Unifying Misinformation Detection

Nayeon Lee, Belinda Z. Li, Sinong Wang, Pascale Fung, Hao Ma, Wen-tau Yih, Madian Khabsa


Abstract
In this paper, we introduce UnifiedM2, a general-purpose misinformation model that jointly models multiple domains of misinformation with a single, unified setup. The model is trained to handle four tasks: detecting news bias, clickbait, fake news, and verifying rumors. By grouping these tasks together, UnifiedM2 learns a richer representation of misinformation, which leads to state-of-the-art or comparable performance across all tasks. Furthermore, we demonstrate that UnifiedM2’s learned representation is helpful for few-shot learning of unseen misinformation tasks/datasets and the model’s generalizability to unseen events.
Anthology ID:
2021.naacl-main.432
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5479–5485
Language:
URL:
https://aclanthology.org/2021.naacl-main.432
DOI:
10.18653/v1/2021.naacl-main.432
Bibkey:
Cite (ACL):
Nayeon Lee, Belinda Z. Li, Sinong Wang, Pascale Fung, Hao Ma, Wen-tau Yih, and Madian Khabsa. 2021. On Unifying Misinformation Detection. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5479–5485, Online. Association for Computational Linguistics.
Cite (Informal):
On Unifying Misinformation Detection (Lee et al., NAACL 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/2021.naacl-main.432.pdf
Data
BASIL