Tracking Attention of Social Media Event by Hidden Markov Model–Cases from Sina Weibo

Abstract

Sina Weibo has significantly impact on the information diffusion processes in many realworld 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 nonredundancy 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.