Identifying influential users by their postings in social networks

Abstract

Much research effort has been conducted to analyze information from social networks, including finding the influential users. In this paper, we propose a graph model to represent the relationships between online posts of one topic, in order to identify the influential users. Besides the role of starters, we suggest a new role, the connecter, to help bridging two different clusters of posts. Three methods for measuring the influences of online posts are discussed to distinguish starters and connecters in the graph. The results of the different measurements can then be integrated to determine the most influential posts and their respective authors. With the information of the explicit and implicit relationship between posts, our model tries to identify the most influential users based on their direct interaction as well as the implicit relationship among postings. The experiment is performed on Twitter to verify the model and the three methods of influence measurement. The interpretation of the methods is also given to justify the experiment results.