Friend circle-based dynamic microblog forwarding behavior prediction system and method

Abstract

The invention relates to a friend circle-based dynamic microblog forwarding behavior prediction system and method and belongs to the social network information analysis field. On the basis of the relationships of users and the behavior data of the users in a social network, the effects of interest differences, the historical behaviors of the users, network structures and the like in promoting network information dissemination are considered; based on the interests, activity degrees and influences of the users, the basis and idea and method of an LDA theme model are adopted to perform modeling analysis on the behaviors of the users, and theme distribution concerning the behaviors of the users is obtained; Gaussian distribution is adopted to improve the LDA, so that the activity degrees and influences of the users can be discovered; based on a principle that the activity degrees of the users change with the time, time discretization and time slicing methods are utilized, so that an improved LDA dynamic microblog forwarding behavior prediction model is put forward; the model is fitted; data are inputted into the prediction model, so that the activity degrees of the users can be monitored dynamically; and therefore, the forwarding behaviors of the users and key factors that affect the forwarding of the users can be predicted more accurately.

Description

Technical field [0001] The invention relates to the field of social network information analysis, and mainly relates to constructing a dynamic microblog forwarding behavior prediction model based on social network user behavior analysis. Background technique [0002] With the popularization of the WEB2.0 concept and the increasing maturity of related technologies, social networking sites such as Twitter, Facebook, and Sina Weibo have had a huge impact on people's lives. People update their status on social networking sites or send broadcasts to show their life status, express their thoughts, or share information with friends. Social networking sites provide a very convenient platform for users to communicate with each other, express opinions and opinions. Modeling and predicting the user behavior of social networking sites has very important social significance and application value for many fields such as security and business, and has gradually attracted the attention of resea...

AI Technical Summary

Problems solved by technology

[0004] The user's information forwarding behavior is the result of multiple factors. However, the above-mentioned prior art does not take into account the complexity of user behavior, and only focuses on predicting user forwarding behavior on the one hand. The prediction results are not accurate, and it is impossible to evaluate the influence of user behavior. The importance of each feature

In addition, current research mainly focuses on the influence of network static characteristics on information dissemination, but ignores the important role of network dynamic characteristics, resulting in the problem of dynamic network static.

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