Taxonomy and Evaluation for Microblog Popularity Prediction

Abstract

As social networks become a major source of information, predicting the outcome of information diffusion has appeared intriguing to both researchers and practitioners. By organizing and categorizing the joint efforts of numerous studies on popularity prediction, this article presents a hierarchical taxonomy and helps to establish a systematic overview of popularity prediction methods for microblog. Specifically, we uncover three lines of thoughts: the feature-based approach, time-series modelling, and the collaborative filtering approach and analyse them, respectively. Furthermore, we also categorize prediction methods based on their underlying rationale: whether they attempt to model the motivation of users or monitor the early responses. Finally, we put these prediction methods to test by performing experiments on real-life data collected from popular social networks Twitter and Weibo. We compare the methods in terms of accuracy, efficiency, timeliness, robustness, and bias. As far as we are concerned, there is no precedented survey aimed at microblog popularity prediction at the time of submission. By establishing a taxonomy and evaluation for the first time, we hope to provide an in-depth review of state-of-the-art prediction methods and point out directions for further research. Our evaluations show that time-series modelling has the advantage of high accuracy and the ability to improve over time. The feature-based methods using only temporal features performs nearly as well as using all possible features, producing average results. This suggests that temporal features do have strong predictive power and that power is better exploited with time-series models. On the other hand, this implies that we know little about the future popularity of an item before it is posted, which may be the focus of further research.