A novel fine-grained rumor detection algorithm with attention mechanism

Abstract

Rumors circulating on social media platforms have consistently represented a substantial threat to societal security and stability. Both academia and the industry have dedicated heightened focus to addressing the issue of rumor detection. Recent research has made significant progress in using deep neural networks to model the textual content and propagation structure of rumors. However, these methods model rumor-related features at a coarse-grained level and do not take full advantage of the various contextual information associated with rumors. In this paper, we propose a new model called Hybrid Rumor Detection Model with Co-Attention Mechanisms(CoAHRD), which utilizes the original tweet content, social context information, and user information for rumor detection. First, we use the Fine-Grained Feature Learning(FGFL) algorithm to extract fine-grained features from the tweets. Based on this, we use the Graph Convolutional Network(GCN) to learn the Multi-relation graph propagation structure features of rumors. Next, we combine FGFL features with temporal encoding information to model the temporal structure of rumors. Then, we introduce a User Feature-Based Co-Attention Network(UFCoAN) to learn the tendency of different users to spread rumors. Finally, we fuse the above features through a fully connected layer and perform rumor detection. Extensive experiments on two publicly available datasets, PHEME and TWITTER15, show that our method outperforms the current mainstream methods. In particular, in terms of accuracy, our model improves by 0.9% and 1.6% over the best baseline method on the Twitter15 and PHEME datasets, respectively.