A Unified Propagation Forest-based Framework for Fake News Detection (2022.coling-1)
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| Challenge: | Recent studies on fake news detection have focused on textual news material, but there is a lack of authoritative regulators. |
| Approach: | They propose a framework to explore latent correlations between propagation trees and a root-induced training strategy to encourage representations of propagation tree to be closer to their prototypical root nodes. |
| Outcome: | The proposed framework explores latent correlations between propagation trees to improve fake news detection. |
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| Challenge: | Graph Neural Networks (GNNs) are used to train neural networks to detect fake news based on context-based methods. |
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| Challenge: | detecting fake news is challenging especially in the era of social media, as it is written intentionally to mislead readers. |
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Entity-Aware Dual Co-Attention Network for Fake News Detection (2023.findings-eacl)
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| Challenge: | Existing models for fake news detection are limited in their ability to detect it from different aspects. |
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