Rumor-rumor refuting game propagation control method based on sparse representation and tensor completion

Abstract

The invention belongs to the field of network public opinion analysis, and particularly relates to a rumor refuting game propagation control method based on sparse representation and tensor complementation, which comprises the following steps: acquiring user data information, and preprocessing the user data information; extracting related attributes of the preprocessed user data information; inputting related attributes of the user data information into a rumor-refuting game propagation model based on sparse representation and tensor completion, and predicting a rumor propagation trend of theuser; and according to the predicted rumor propagation trend of the user, controlling the rumor propagation user to prevent rumor propagation. According to the method, the evolutionary game theory andthe neural network are used for predicting whether the user participates in the rumor-refuting topic or not, the time when the user participates in topic discussion can be dynamically predicted, andsituation awareness is carried out on the rumor topic development trend.

Description

technical field [0001] The invention belongs to the field of network public opinion analysis, and in particular relates to a rumor-refuting game propagation control method based on sparse representation and tensor completion. Background technique [0002] Internet rumors refer to offensive and purposeful network information that has no factual basis and is spread through network media (such as twitter). With the rapid development of the Internet and the promotion of social media, social media has the characteristics of freedom, interaction, diversity, speed, and popularity, which makes it easier to generate and spread online rumors. When spread in large quantities, it usually causes panic to the masses of the people, and also has a bad impact on the social economy and social order. Carrying out the research on the spread prediction model of rumors and rumor refuting topics will help to grasp the distribution of group forwarding characteristics, which is of great significanc...

AI Technical Summary

Problems solved by technology

[0004] Understanding the law of rumor propagation is the key issue of rumor control, but most studies on rumor propagation models ignore the sparsity of valid data in the rumor propagation space, resulting in one-sided conclusions, the paper Chen J, Wu Y, Fan L, et al. N2VSCDNNR: A Local Recommender System Based on Node2vec and Rich Information Network [J]. IEEE Transactions on Computational Social Systems, 2019. Using node2vec to capture complex latent relationships between users from the corresponding network to Alleviate data sparsity problem

However, this paper only considered the influence factors of users and rumor information itself, ignoring the influence of other information in the propagation space at the same time on the spread of rumors, resulting in poor follow-up control of rumors

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