Predicting Retweet Behavior in Online Social Networks Based on Locally Available Information

摘要

Behavior prediction in online social networks (OSNs) has attracted lots of attention due to its vast applications. However, most previous work needs global network information to train classifiers. Due to the large data volume and privacy concern, it is infeasible to obtain global network information for every OSN. We propose a decentralized framework, named REPULSE, to predict whether a target user will retweet a message relayed by his friends. We also identify a new set of community-related features that improve retweet prediction accuracy considerably. To demonstrate the value of community-related features, we propose another framework named HOTPIE to predict tweets popularity. Utilizing community-related features can boost the F1 score of popularity prediction from 0.43 to 0.55. To the best of our knowledge, this is the first work which systematically studies the impact of global vs. locally observable information on the prediction of retweet behavior in OSNs.