Microblog popularity prediction model based on time and forwarding sequence of user

Abstract

The invention relates to a microblog popularity prediction model based on time and a forwarding sequence of a user, which belongs to the field of message popularity prediction in a social network, andcomprises the following steps of: S1, modeling the forwarding sequence of a microblog by using a recurrent neural network to capture long-distance dependence of a message propagation process; s2, performing nonlinear network transformation on an output result of the hidden layer, and learning the rate of each time step in the propagation process; and S3, utilizing the early trend acceleration andthe early popularity obtained by the rate, and predicting the future popularity of the microblog under the optimization of the user activity. According to the method, it is guaranteed that the popular trend of the microblog in the future is more accurately predicted in the early stage of message propagation, and the model not only utilizes historical propagation information, but also well describes the propagation process of the microblog.

Description

technical field [0001] The invention belongs to the field of message popularity prediction in social networks, and relates to a microblog popularity prediction model based on time and user forwarding sequence5 Background technique [0002] The ubiquity and cheapness of Web 2.0 services has changed the way content is generated and consumed online. In recent years, Internet technology has developed rapidly. With the rapid rise and popularization of the Internet, our lives are now inseparable from the Internet. Due to the Internet, content producers can use traditional channels to reach audiences of unimaginable scale, and realize the services of communication between content producers and consumers on a global scale, including video, photo, music sharing, weblogs, social bookmarking sites, Collaborative portal and content submission, browsing, content news aggregator rating and discussion etc. Social networking services represented by Facebook, Twitter, Weibo, WeChat, etc. h...

AI Technical Summary

Problems solved by technology

Models based on classification or regression rely on the extraction of features and do not describe the process of message dissemination. The performance of methods based on point random processes is lacking, and due to the diversification of social networks, they cannot be adapted to every social network. And there is no supervision using historical information

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