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Unsupervised Fake News Detection on Social Media: A Generative Approach
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| model user credibility and second-level engagements in a Bayesian network (UFD) | unsupervised fake news detection · LIAR | accuracy: 0.641 -> 0.759 | Helped |
| UFD vs truth discovery baselines | unsupervised fake news detection · BuzzFeed | accuracy: 0.562 -> 0.679 | Helped |
| UFD vs TruthFinder | unsupervised fake news detection · BuzzFeed | Fake recall: 0.720 -> 0.643 | Hurt |
| lower prior true positive count / higher prior false positive count for verified users (ablation) | UFD · LIAR, BuzzFeed | F1 | Hurt |
| weight opinions by credibility instead of majority voting | opinion aggregation · LIAR | accuracy: 0.586 -> 0.759 | Helped |
Closest papers by meaning
- Each Fake News Is Fake in Its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection (AAAI 2025 · similarity 0.87)
- Contradicted in Reliable, Replicated in Unreliable: Dual-Source Reference for Fake News Early Detection (AAAI 2025 · similarity 0.87)
- Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection (AAAI 2024 · similarity 0.87)
- Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation Learning (AAAI 2024 · similarity 0.87)
- Learning Complex Heterogeneous Multimodal Fake News via Social Latent Network Inference (AAAI 2025 · similarity 0.87)
- Unveiling Implicit Deceptive Patterns in Multi-Modal Fake News via Neuro-Symbolic Reasoning (AAAI 2024 · similarity 0.85)
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