Rumour Detection on Social Media with Long-Tail Strategy

With the popularity of the mobile Internet, the proliferation of false rumors on social media has caused significant losses to the government and the public. Rumor detection on social media has become the critical research content. Recently, scholars have taken advantage of Graph Neural Networks (GNNs) to learn the textual features and propagation structure of rumors. On social media, only a few users have lots of retweets, and most real users belong to the long-tail users, who are low degree nodes in the social network. However, the existing graph convolution network would be more biased towards high degree nodes and ignore low degree nodes. We propose a new long-tail strategy based on the improved transformer that enhances the model's learning about long-tail users' features. Our long-tail strategy is a general approach that works well with existing GNN approaches. We also perform contrastive learning by combining sparse and dense attention to capture interaction features. Through extensive comparative and ablation experiments, we achieved state-of-the-art results and demonstrated the effectiveness of each module on two real-world datasets.