A graph-based pivotal semantic mining framework for rumor detection

As the gathering place for modern content sharing, social platforms not only provide places for people to obtain information and express opinions, but also provide hidden channels for the generation and dissemination of rumors. Rumor detection is an important and challenging task for mining fake information in social networks. Previous methods utilized sequential models to embed semantic features, which are not comprehensive for mining pivotal semantic information and easy to ignore discontinuous dependencies. However, excessive mining of information content will lead to the acquisition of some redundant information, whose existence will affect the extraction of useful features and the detection ability of the model. To address these issues, this paper offers a graph-based pivotal semantic mining framework. Specifically, we model the content information as a graph structure, learn the semantic dependencies across segments through a gated graph neural network, and co-learn by combining the propagation features of rumors. Furthermore, in order to highlight the precious essential semantic information, a shared unit cell is offered to reduce the influence of redundant information. Experimental results on realworld datasets show that the proposed method exceeds existing methods in terms of benchmark testing.