On Unifying Misinformation Detection
This work addresses misinformation detection for various domains, offering a unified approach that improves performance and generalizability, though it is incremental as it builds on existing tasks.
The paper tackles the problem of detecting misinformation across multiple domains by introducing UnifiedM2, a model that jointly handles news bias, clickbait, fake news, and rumor verification, achieving state-of-the-art or comparable performance on all tasks.
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.