•  
  •  
 

Applied Mathematics & Information Sciences

Author Country (or Countries)

Lebanon

Abstract

Due to the spread of technology and world wide web, Online Social media invaded every home in the world; hence, the analysis of such networks became an important yet challenging case of study for researchers. One of the most interesting fields of study in social network analysis is identifying influential users who are important actors in online social networks by having an impact on others. This work investigates the problem of identifying influential users on Twitter. Since Twitter is a user-friendly interactive platform, it became an apparent competitor to other social medias as far as user interaction. Twitter is browsed by a variety of users, the most important are the most influential ones among them all. In order to identify influential users, a data set was collected between December 2015 and March 2016 reflecting real tweets from the top trendy hashtags on Twitter. In this paper, different measures are used such as Influence Measures, Centrality Measures and Activity Measures. In addition, Association Rule Learning has been used to detect relationships between users. After identifying the influential users from Association Rule Learning, these influential users were compared to the results of the abovementioned measures. The results of this study indicate that identifying influential users from Association Rule Learning and validating these identified users with the results of Influence Measures is an efficient method for detecting the influence of users on online social networks.

Digital Object Identifier (DOI)

http://dx.doi.org/10.18576/amis/120515

Download

Share

COinS