On Unifying Misinformation Detection

• URL: http://arxiv.org/abs/2104.05243v1
• Date: Mon, 12 Apr 2021 07:25:49 GMT
• Title: On Unifying Misinformation Detection
• Authors: Nayeon Lee, Belinda Z. Li, Sinong Wang, Pascale Fung, Hao Ma, Wen-tau Yih, Madian Khabsa
• Abstract summary: The model is trained to handle four tasks: detecting news bias, clickbait, fake news, and verifying rumors. We demonstrate that UnifiedM2's learned representation is helpful for few-shot learning of unseen misinformation tasks/datasets.
• Score: 51.10764477798503
• License: http://creativecommons.org/licenses/by/4.0/
• 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, UnifiedM2learns 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 model's generalizability to unseen events.

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