Retweeting Behavior Prediction Based on One-Class Collaborative Filtering in Social Networks

Abstract

Social behaviors such as retweetings, comments or likes are valuable information for human activities analysis. We focus here on user's retweeting behavior which has been considered as a key mechanism of information diffusion in social networks. Since we can only observe on which messages user retweet. It is a typically one-class setting which only positive examples or implicit feedback can be observed. However, few research works on retweeting prediction consider one-class setting. In this paper, we analyze and study the fundamental factors that might affect retweetability of a tweet, and then employ one-class collaborative filtering method by quantitatively measure the user personal preference and social influence between users and messages to predict user's retweeting behavior. Experimental results on a real-world dataset from social network show that the proposed method is effective and can improve the performance of the one-class collaborative filtering over baseline methods through leveraging weighted negative examples information.