Research map / Detection and segmentation / Weakly supervised detection and segmentation
Weak Supervision for Fake News Detection via Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add a reinforced selector to filter weakly labeled samples (ablation) | WeFEND · WeChat dataset | Accuracy: 0.807 -> 0.824 | Helped |
| Add a reinforced selector to filter weakly labeled samples (ablation) | WeFEND · WeChat dataset | AUC-ROC: 0.858 -> 0.873 | Helped |
| Use user-report weak supervision instead of labeled training data alone | CNN · WeChat dataset | Accuracy: 0.747 -> 0.759 | Helped |
| Use user-report weak supervision instead of labeled training data alone | LSTM · WeChat dataset | Accuracy: 0.733 -> 0.762 | Helped |
| Use unlabeled data with entropy minimization | CNN · WeChat dataset | Accuracy: 0.747 -> 0.759 | Helped |
| Use unlabeled data with entropy minimization | LSTM · WeChat dataset | Accuracy: 0.733 -> 0.753 | Helped |
| Use automatically annotated weak labels without the reinforced selector | WeFEND- · WeChat dataset | Fake news precision: 0.869 -> 0.846 | Hurt |
Closest papers by meaning
- Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection (AAAI 2024 · similarity 0.86)
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- Collaborative Evolution: Multi-Round Learning Between Large and Small Language Models for Emergent Fake News Detection (AAAI 2025 · similarity 0.83)
- Reinforced Adaptive Knowledge Learning for Multimodal Fake News Detection (AAAI 2024 · similarity 0.83)
- Learning Complex Heterogeneous Multimodal Fake News via Social Latent Network Inference (AAAI 2025 · similarity 0.82)
- GAMC: An Unsupervised Method for Fake News Detection Using Graph Autoencoder with Masking (AAAI 2024 · similarity 0.82)
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