Popularity prediction in microblogging network

Predicting the popularity of content is important for both the host and users of social media sites. The challenge of this problem comes from the inequality of the popularity of content. Existing methods for popularity prediction are mainly based on the quality of content, the interface of social media site to highlight contents, and the collective behavior of users. However, little attention is paid to the structural characteristics of the networks spanned by early adopters, i.e., the users who view or forward the content in the early stage of content dissemination. In this paper, taking the Sina Weibo as a case, we empirically study whether structural characteristics can provide clues for the popularity of short messages. We find that the popularity of content is well reflected by the structural diversity of the early adopters. Experimental results demonstrate that the prediction accuracy is significantly improved by incorporating the factor of structural diversity into existing methods.