Estimating Twitter Influential Users by Using Cluster-Based Fusion Methods

Abstract

A considerable part of social network analysis literature is dedicated to determining which individuals are to be considered as influential in particular social settings. Concretely, Social Influence can be described as the power or even the ability of a person to yet influence the thoughts as well as the actions of other users. So, User Influence stands as a value that depends on the interest of the followers of a concrete user (via retweets, replies, mentions, favorites, etc.). This paper focuses on identifying such phenomena on the Twitter graph and on presenting a novel methodology for characterizing Twitter Influential Users. The novelty of our approach lies in the fact that we have incorporated a set of features for characterizing social media authors, including both nodal and topical metrics, along with new features concerning temporal aspects of user participation on the topic. We have also implemented cluster-based fusion techniques in order to retrieve result lists for the ranking of top influential users. Hence, results show that the proposed implementations and methodology can assist in identifying influential users, that play a dominant role in information diffusion.