Research map / Time series / Anomaly detection
Early Detection of Fake News on Social Media Through Propagation Path Classification with Recurrent and Convolutional Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| classify propagation paths of user characteristics instead of linguistic/structural features | fake news detection · Twitter15 | accuracy: 0.750 -> 0.842 | Helped |
| classify propagation paths of user characteristics | fake news detection · Twitter16 | accuracy: 0.732 -> 0.863 | Helped |
| classify propagation paths of user characteristics | fake news detection · Weibo | accuracy: 0.910 -> 0.921 | Helped |
| add CNN branch to RNN-only model (ablation) | PPC · Twitter15 | accuracy: 0.811 -> 0.842 | Helped |
| add RNN branch to CNN-only model (ablation) | PPC · Twitter16 | accuracy: 0.847 -> 0.863 | Helped |
| shorten detection deadline below 24 hours | PPC vs DTR/GRU/PTK · Twitter15/Twitter16/Weibo | accuracy | Mixed |
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- Contradicted in Reliable, Replicated in Unreliable: Dual-Source Reference for Fake News Early Detection (AAAI 2025 · similarity 0.84)
- Each Fake News Is Fake in Its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection (AAAI 2025 · similarity 0.84)
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