A New Approach for Predicting an Important User on a Topic on Twitter

Abstract

Twitter is an online social networking service with millions of users and an impressive flow of messages that are published and spread daily through interactions among users. There are different types of users on Twitter; therefore, determining the most important users in each topic is highly challenging. Hence, it is necessary to define efficient computed measures to classify users according to the criteria of relevance and the possibility of representing reality. Although several studies have considered identifying the user influence, user popularity, or user activity in a social network, relatively less focus has been on measuring and predicting important users in case of a topic. In this study, we have proposed a method to determine an important user based on the activities related to each topic on Twitter by combining the measures related to user influence, user activity, and user popularity. The results verified the effectiveness of our proposed approach for the identification of important users in each topic.