Summary of Out-of-distribution Rumor Detection Via Test-time Adaptation, by Xiang Tao et al.
Out-of-distribution Rumor Detection via Test-Time Adaptation
by Xiang Tao, Mingqing Zhang, Qiang Liu, Shu Wu, Liang Wang
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method, TARD (Test-time Adaptation for Rumor Detection under distribution shifts), tackles the issue of rumor detection in Out-Of-Distribution (OOD) situations. Existing methods struggle when facing real-world test data due to significant distribution shifts caused by differences in news topics, social media platforms, languages, and propagation scale. To address this challenge, TARD models the news propagation as a graph and builds an adaptation framework, enhancing the model’s adaptability and robustness. The method outperforms state-of-the-art methods on two real-world datasets collected from social platforms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TARD is a new way to help computers detect rumors better when they’re different from what they’ve seen before. Right now, rumor detectors are good at finding rumors in the same type of data they were trained on. But when the data changes – like when news topics or languages change – these detectors don’t do as well. TARD solves this problem by looking at how news spreads and making its detector more flexible. This helps it work better in real-world situations where data might be different. |
