Influence propagation model establishing method based on viewpoint vectorization

Abstract

The invention discloses an establishment method of an influence propagation model based on viewpoint vectorization. An existing method has limitation, a model cannot effectively calculate the influence among users, and a certain effect can only be achieved in a single social network or group environment generally. The method comprises the following steps: crawling related information of original content of a user in a social network, and establishing a propagation network based on viewpoint influence; searching a local influence user set consistent with the user viewpoint in the influence propagation network by utilizing random walk; searching a global influence user set similar to the user viewpoints in the influence propagation network according to the viewpoint similarity; searching a user set opposite to the user viewpoint in the influence propagation network; and finally, establishing an influence propagation model based on viewpoints. According to the method, the influence user set opposite to the user viewpoint is considered, and the influence propagation model based on the viewpoint can be more accurately reflected.

Description

technical field [0001] The invention belongs to the technical field of the Internet, in particular to the technical field of social network analysis, and specifically relates to a method for establishing an influence propagation model based on viewpoint vectorization. Background technique [0002] Social network influence is a manifestation of an individual's influence on the behavior of other users by publishing or reposting opinion content on the Internet platform, specifically manifested in likes, reposts, etc. In recent years, with the rapid development and popularization of computer technology, more and more users express their opinions and spread content on social networking platforms such as Twitter (Twitter) and Weibo, and influence other users. Around the aspect of social influence between users, researchers have carried out a lot of work and realized a variety of applications, including marketing, advertising and public opinion control and many other fields. [00...

AI Technical Summary

Problems solved by technology

However, this approach has many limitations

First of all, there are limitations in the definition of edges. The existence of an edge can only be determined when there is a concern relationship between users, or there is a corresponding interaction between users. Therefore, this method cannot predict the influence of all users. ;Secondly, although there are a large number of edges in the social network, compared with the total number of users, the edge data is still sparse, and the model cannot effectively calculate the influence between users; finally, many influences are calculated through edges. The power method is realized by mining the strong features in the social network. This method is not universal and usually only has a certain effect in a single social network or group environment.

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