Summary of A (more) Realistic Evaluation Setup For Generalisation Of Community Models on Malicious Content Detection, by Ivo Verhoeven et al.
A (More) Realistic Evaluation Setup for Generalisation of Community Models on Malicious Content Detection
by Ivo Verhoeven, Pushkar Mishra, Rahel Beloch, Helen Yannakoudakis, Ekaterina Shutova
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Social and Information Networks (cs.SI)
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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 A novel evaluation setup for malicious content detection is proposed, which addresses limitations of current benchmark datasets by incorporating social graph context and testing model generalization through few-shot subgraph sampling. This approach simulates real-world scenarios where models are evaluated on unseen tasks, domains, or graph structures. Graph meta-learners trained with this method outperform standard community models in detecting misinformation and hate speech. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research proposes a new way to test how well AI models can detect online misinformation and hate speech by training them on small groups of labeled examples within larger social networks. This approach helps the models generalize better to new situations they haven’t seen before, which is important because online content is constantly changing. The results show that this method leads to more accurate detection than traditional approaches. |
Keywords
* Artificial intelligence * Few shot * Generalization
