Prediction Based on the Acquisition of Sina Weibo Data and Users’ Forwarding Behavior

Abstract

Microblogging, also known as Weibo, has currently become the prevalent social networking platform, and its function of disseminating information has been increasingly recognized and valued. One of the main approaches of information communication on Weibo is forwarding activities among users. Therefore, the key interests have focused on the microblogs that are easily recognized and forwarded. Through predicting the possibility of a micro-blog being forwarded among users, it is able to effectively improve the efficiency of information spreading on the Weibo platform. On the basis of Sina Weibo data, this paper intends to study 13 features of Sina Weibo including user and content, so as to conduct machine learning, to establish a feature analysis model, to identify key influence factors on Weibo forwarding, to study the combination of feature attributes for the first time, to explore the correlation among features, then to predict the probability of a micro-blog being forwarded by users. Meanwhile, concerning different features held by different data, different machine learning algorithms are applied in model training, in order to identify the algorithm with the highest prediction accuracy.