Rumor Diffusion Model Based on Representation Learning and Anti-Rumor

Abstract

The traditional rumor diffusion model primarily studies the rumor itself and user behavior as the entry points. The complexity of user behavior, multidimensionality of the communication space, imbalance of the data samples, and symbiosis and competition between rumor and anti-rumor are challenges associated with the in-depth study on rumor communication. Given these challenges, this study proposes a group behavior model for rumor and anti-rumor. First, this study considers the diversity and complexity of the rumor propagation feature space and the advantages of representation learning in the feature extraction of data. Further, we adopt the corresponding representation learning methods for their content and structure of the rumor and anti-rumor to reduce the spatial feature dimension of the rumor-spreading data and to uniformly and densely express the full-featured information feature representation. Second, this paper introduces an evolutionary game theory, which is combined with the user-influenced rumor and anti-rumor, to reflect the conflict and symbiotic relationship between rumor and anti-rumor. we obtain a network structural feature expression of the influence degree of users on rumor and anti-rumor when expressing the structural characteristics of group communication relationships. Finally, aiming at the timeliness of rumor topic evolution, the whole model is proposed. Time slice and discretize the life cycle of rumor is used to synthesize the full-featured information feature representation of rumor and anti-rumor. The experiments denote that the model can not only effectively analyze user group behavior regarding rumor but also accurately reflect the competition and symbiotic relation between rumor and anti-rumor diffusion.