Rumor Detection Based on Knowledge Enhancement and Graph Attention Network

Presently, most of the existing rumor detection methods focus on learning and integrating various features for detection, but due to the complexity of the language, these models often rarely consider the relationship between the parts of speech. For the first time, this paper integrated a knowledge graphs and graph attention networks to solve this problem through attention mechanisms. A knowledge graphs can be the most effective and intuitive expression of relationships between entities, providing problem analysis from the perspective of “relationships†. This paper used knowledge graphs to enhance topics and learn the text features by using self-attention. Furthermore, this paper defined a common dependent tree structure, and then the ordinary dependency trees were reshaped to make it generate a motif-dependent tree. A graph attention network was adopted to collect feature representations derived from the corresponding syntax-dependent tree production. The attention mechanism was an allocation mechanism of weight parameters that could help the model capture important information. Rumors were then detected accordingly by using the attention mechanism to combine text representations learned from self-attention and graph representations learned from the graph attention network. Finally, numerous experiments were performed on the standard dataset Twitter, and the proposed model here had achieved a 7.7% improved accuracy rate compared with the benchmark model.