Propagation Model Of Periodic Topic On Weibo

According to statistics, 45.5 percent of Internet users use weibo. Weibo allows information dissemination on social network change from traditional one-way to multi-directional and interactive, and people can proactively publish news instead of being accepted passively. Based on these characteristics, it makes the spread of rumors also entered the fast lane. Some uninformed weibo listeners may be agitated and bewitched by the rumors and make radical behavior or publish radical remarks. Based on the mode of transmission and the propagation model of periodic topic in the weibo, we can roughly understand the way of periodic topic transmission. Thus, we can predict the dissemination trend of information in weibo and other social networks when we meet periodic events in the future, and make the judgment and correct treatment for the guidance of public opinion, and provide reference for analysis of social phenomena.This paper designs and implements a set of software for Tencent weibo data acquisition. Based on OAuth2.0 authentication mechanism and coded by cross-platform language Java, the software use Tencent weibo open API to search topics and save related weibo data by using the method of directly access weibo information and user ID. Then this paper makes the special form of mathematical transformation on the obtained data of specific keywords from the perspective of statistical analysis, and makes the curve fitting statistics analysis to show the data with chart. And then, this paper analyzes two factors: forwarding weibo and original weibo, which impacting propagation model. Then, this paper establishes respectively describes their dissemination trend model, obtains the total trend of weibo propagation model, and finally establishes propagation model in weibo periodic topic.Selecting a plurality of periodic topics,this paper uses the resulting model to describe its weibo totality variation with time, and then carries on the examination to the fitting results with the captured actual data. This paper verifies the correctness of the model and arrives the following conclusion: Propagation model in the social network of periodic topic is a combination of periodic propagation model and sudden topic propagation model.