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.
@article{arxiv.2104.05243,
title = {On Unifying Misinformation Detection},
author = {Nayeon Lee and Belinda Z. Li and Sinong Wang and Pascale Fung and Hao Ma and Wen-tau Yih and Madian Khabsa},
journal= {arXiv preprint arXiv:2104.05243},
year = {2021}
}