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arXiv · 2104.05243

On Unifying Misinformation Detection

Nayeon Lee·Belinda Z. Li·Sinong Wang·Pascale Fung·Hao Ma·Wen-tau Yih·Madian Khabsa
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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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Nayeon Lee, Belinda Z. Li, Sinong Wang, Pascale Fung, Hao Ma, Wen-tau Yih, Madian Khabsa. 2021-04-12. On Unifying Misinformation Detection. https://arxiv.org/abs/2104.05243

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