Network negative information impact minimization method based on topic model

Abstract

The invention relates to a network negative information impact minimization method based on a topic model. The network negative information impact minimization method comprises the following steps: 1) adopting a directed graph to express the propagation of information in a social network, and independently calculating the probability distribution of the negative information and the probability distribution of historical information on each edge through the topic model; 2) independently calculating a distance, i.e., KL (Kullback-Leibler) divergence d(w,i), between the probability distribution of the negative information and the probability distribution of historical information on each edge, wherein d expresses a calculation result of the KL divergence, w expresses the topic distribution of the historical information, and i expresses the topic distribution of the negative information; 3) calculating tb(w)=b(w) / d(w,i) and to(w)=o(w) / d(w,i), wherein b(w) and o(w) are independently the calculation results of a centrality algorithm and an out-degree in-degree algorithm and are sorted from big to small, and the first k nodes are removed to minimize the propagation range of the negative information. The method disclosed by the invention can effectively control the social network of which the malicious information is burst, and greatly reduces an impact range of the negative information.

Description

technical field [0001] The invention belongs to the fields of network technology and information technology, and in particular relates to a method for minimizing the influence of negative network information based on a topic model. Background technique [0002] Over the past few decades, online social networks have provided convenient platforms for information dissemination and marketing activities, allowing ideas and behaviors to flow to each other in cascades of social relationships. Judging from the functions of social networks, it can not only spread positive news, such as: innovative ideas, hot topics, etc., but also spread negative news, such as: malicious rumors, false information, etc. Taking rumors as an example, even if there are only a few infected people at the beginning, the number of infected people will eventually be large due to the triggering of a series of cascading structures in the network. Therefore, how to design an effective method to reduce the scope...

AI Technical Summary

Problems solved by technology

However, his approach does not take into account what to do with an already infected network

Images