Abstract
Sina Weibo has significantly impact on the information diffusion processes in many real-world social events. A large number of active users on Sina Weibo not only push the opinion diffusion, but also increase the influence abilities of events which conversely attracted much attentions of users to follow them. How to effectively track the event attention of users is one of the most important channels to get the public opinions. In order to predict the event attention more accurately, motivated by observations of social events’ influence concerning with users and microblogs, we quantify the user popularity from the four dimensions: the user activity, the user behavior, the user authenticity and the user infection ability. And the non-collinearity of these four dimensions is tested to ensure the comprehensiveness and non-redundancy of the evaluation. Then, combining with the logic framework of Hidden Markov Model, we propose an algorithm to predict the Weibo event attention by using the user popularity. Meanwhile, in order to better detect the performance of the prediction algorithm, we integrate the static and dynamic information of microblog content to directly quantify the current Weibo event attention as a benchmark, and the performance of four prediction algorithms (including our algorithm) is tested with six real data sets which are chosen from the popular events in China from 2019 to 2020. Through comparison, we find that the user popularity can be used to predict the event attention, and the Hidden Markov Model prediction method by using the user popularity shows good prediction performance.
Highlights
In recent years, with the rapid development of the Internet and the popularity of intelligent terminals, the Sina Weibo platform emerge in China as the times require
Three contributions are presented in this work: (1) we present a user popularity metric to measure user influence by combining comprehensively the characteristics of microblogs and users; (2) the Weibo event attention degree is defined on original microblogs and retweet microblogs; (3) in the framework of Hidden Markov Model (HMM), the original observation sequence is used to predict the hidden state sequence (Weibo event attention)
The user popularity state sequence is used as the observation variable, and the initial parameters λ0 = (A0, B0, π0) are input into the Baum-Welch algorithm to learn the parameters λ = (A, B, π) of the HMM, and the Hidden Markov Model of each Weibo event is obtained
Summary
With the rapid development of the Internet and the popularity of intelligent terminals, the Sina Weibo platform emerge in China as the times require. This platform provide a large number of new ways of information exchange for the public, and become an important channel for people to obtain information [1]. The massive and rapid dissemination of information in Sina Weibo platform is a double-edged sword for the development of society. The massive dissemination of false information in the Weibo platform will have a bad impact on society, and if such information is not controlled in time, it will cause a crisis [3].
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