Research map / Label-efficient and robust learning / Domain adaptation and generalization
Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed domain-specific and cross-domain model instead of EANN-Multimodal | Cross-domain fake news detection · PolitiFact | F1: 0.798 -> 0.835 | Helped |
| Use the proposed domain-specific and cross-domain model instead of EANN-Multimodal | Cross-domain fake news detection · GossipCop | F1: 0.813 -> 0.836 | Helped |
| Use the proposed domain-specific and cross-domain model instead of EANN-Multimodal | Cross-domain fake news detection · CoAID | F1: 0.808 -> 0.869 | Helped |
| Add the domain-shared loss (ablation) | Our Approach · PolitiFact | F1: 0.815 -> 0.835 | Helped |
| Add the domain-specific loss (ablation) | Our Approach · PolitiFact | F1: 0.786 -> 0.835 | Helped |
| Add the network modality (ablation) | Our Approach · GossipCop | F1: 0.746 -> 0.836 | Helped |
| Add the text modality (ablation) | Our Approach · CoAID | F1: 0.707 -> 0.869 | Helped |
| Select training records with LSH-based domain coverage rather than random selection | Fake news detection model trained with selected records · PolitiFact, B/|R_pool| = 0.1 | F1: 24% | Helped |
| Select training records with LSH-based domain coverage rather than random selection | Fake news detection model trained with selected records · CoAID, B/|R_pool| = 0.1 | F1: 27% | Helped |
Closest papers by meaning
- DAMMFND: Domain-Aware Multimodal Multi-view Fake News Detection (AAAI 2025 · similarity 0.91)
- Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection (AAAI 2024 · similarity 0.89)
- Reinforced Adaptive Knowledge Learning for Multimodal Fake News Detection (AAAI 2024 · similarity 0.87)
- Contradicted in Reliable, Replicated in Unreliable: Dual-Source Reference for Fake News Early Detection (AAAI 2025 · similarity 0.87)
- Each Fake News Is Fake in Its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection (AAAI 2025 · similarity 0.87)
- External Reliable Information-enhanced Multimodal Contrastive Learning for Fake News Detection (AAAI 2025 · similarity 0.87)
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