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Claim-Centric and Sentiment Guided Graph Attention Network for Rumour Detection

Automatic rumour detection has gained attention due to the influence of social media on individuals and its pervasiveness. In this work, we construct a representation that takes into account the claim in the source tweet, considering both the propagation graph and the accompanying text alongside tweet sentiment. This is achieved through the implementation of a hierarchical attention mechanism, which not only captures the embedding of documents from individual word vectors but also combines these document representations as nodes within the propagation graph. Furthermore, to address potential overfitting concerns, we employ generative models to augment the existing datasets. This involves rephrasing the claims initially made in the source tweet, thereby creating a more diverse and robust dataset. In addition, we augment the dataset with sentiment labels to improve the performance of the rumour detection task. This holistic and refined approach yields a significant enhancement in the performance of our model across three distinct datasets designed for rumour detection. Quantitative and qualitative analysis proves the effectiveness of our methodology, surpassing the achievements of prior methodologies.