Abstract
In the era of information explosion, rumors will cause great harm and affect social stability. Most rumor detection methods concentrate on extracting features from content and consumer information. We propose a brand-new approach to early rumor identification, MSR-GAT. Firstly, the source text and comment text are fused as node features and the relation between events is considered edge information. Then, the graph attention model is constructed to classify nodes and complete rumor detection. The experimental findings demonstrate that the detection algorithm outperforms the baselines algorithm in accuracy, precision, recall and F1-Measure. It can accurately identify rumors.
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