Popularity Prediction in Microblogging Network: A Case Study on Sina Weibo

TL;DR

This paper investigates how the structural diversity of early adopters in Sina Weibo influences content popularity, demonstrating that incorporating this factor significantly improves prediction accuracy.

Contribution

It introduces the use of structural diversity of early adopters as a new factor for predicting social media content popularity.

Findings

Structural diversity correlates with content popularity.

Incorporating structural diversity improves prediction accuracy.

Empirical analysis on Sina Weibo data supports the approach.

Abstract

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 con- tent. 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 user- s. However, little attention is paid to the structural charac- teristics 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 character- istics 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 signif- icantly improved by incorporating the factor of structural diversity into existing methods.