Abstract

Social influence occurs when one’s opinions, emotions, or behaviors are affected by others in a social network. However, social influence takes many forms, and its underlying mechanism is still unclear. For example, how is one’s behavior influenced by a group of friends who know each other and by the friends from different ego friend circles? In this article, we study the social influence problem in a large microblogging network. Particularly, we consider users’ (re)tweet behaviors and focus on investigating how friends in one’s ego network influence retweet behaviors. We propose a novel notion of social influence locality and develop two instantiation functions based on pairwise influence and structural diversity. The defined influence locality functions have strong predictive power. Without any additional features, we can obtain an F1-score of 71.65% for predicting users’ retweet behaviors by training a logistic regression classifier based on the defined influence locality functions. We incorporate social influence locality into a factor graph model, which can further leverage the network-based correlation. Our experiments on the large microblogging network show that the model significantly improves the precision of retweet prediction. Our analysis also reveals several intriguing discoveries. For example, if you have six friends retweeting a microblog, the average likelihood that you will also retweet it strongly depends on the structure among the six friends: The likelihood will significantly drop (only ⅙) when the six friends do not know each other, compared with the case when the six friends know each other.