  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.
