Network Rumor Detection Based on Enhanced Textual Semantics and Weighted Comment Stance

Social networks, while enabling information exchange among individuals, also serve as fertile grounds for the dissemination of rumors. The succinct nature of social media posts poses a challenge for most rumor detection methods reliant on content semantic features due to the insufficiency of semantic information. Additionally, numerous rumor detection techniques focusing on propagation features often disregard the unique attributes of commenters, leading to inadequate allocation of weights to different user comments. Thus, a network rumor detection approach is proposed, integrating text semantic enhancement and weighted comment stance. Initially, entities and concepts in posts are elucidated via an external knowledge graph to furnish additional contextual information, thereby augmenting semantic comprehension. Subsequently, leveraging pointwise mutual information, the enhanced text is translated into a weighted graph representation, and a weighted graph attention network is employed to assimilate enhanced semantic features of posts. Stance information for each comment within the post is then extracted using a pre-trained stance detection model, with weight values of stance information being learnt based on commenters’ characteristics. Furthermore, temporal data of comment stances and corresponding commenter sequences are fed into a cross-modal Transformer to glean the temporal features of comment stances. Ultimately, the enhanced semantic features are adaptively merged with the weighted temporal features of comment stances and fed into a multi-layer perceptron for classification. Experimental results on the PHEME and Weibo datasets demonstrate that this method not only achieves an accuracy improvement of over 1.6 percentage points compared with the state-of-the-art baseline method but also outperforms the best baseline method by at least 12 hours in early rumor detection.