A Rumor Propagation Prediction Based on Multi-dimensional User Characteristics

Compared to traditional media, the open and instantaneous nature of social media makes it easier for rumors to emerge and spread, thereby causing more severe societal impacts. Therefore, identifying potential user connections within rumor-related topics holds critical significance for public opinion monitoring and social stability. Firstly, according to the richness and multi-level nature of user characteristics, the interests and preferences and social attributes of users are comprehensively analyzed, so as to realize the multi-dimensional and in-depth representation of user characteristics. Secondly, to better capture the complexity of rumor-related social interactions, a rumor user interaction node2vec method that leverages user interaction behavior is proposed. The method uses a biased random walk strategy that combines rumor influence and user interaction evolution. It effectively captures key attributes of the rumor interaction space, leading to a more precise representation of the network structure in rumor propagation. Finally, in view of the complexity of the relationship between different nodes, the graph attention network is used to effectively capture the interaction between nodes, and a social network link prediction method for rumor propagation is proposed. Experiments and analyses are conducted on multiple public datasets. The results demonstrate that the proposed link prediction model can accurately predict whether links will form between users, showing significant advantages in the specific and complex scenario of rumor propagation.