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The study aims to enhance rumor detection on social media by employing a multi-feature graph-based approach.

Multi-feature graphs and contrastive learning for rumor detection on social media

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The study aims to enhance rumor detection on social media by employing a multi-feature graph-based approach.
Constructed four graph structures: co-occurrence, part-of-speech, named entity, and semantic dependency.
Developed a gated MLP-based fusion mechanism to integrate heterogeneous graph features.
Implemented a cluster-driven contrastive learning strategy to improve model discriminative capability.
Achieved superior performance on rumor detection datasets, even with only 1% of training data.
Outperformed existing baselines in terms of accuracy.
Successfully implemented automatic clustering of semantically similar rumors.

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Novel model improves rumor detection in social media, indicating effective integration of diverse features.

Huang et al. (2026) studied this question.

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