Degree centrality, eigenvector centrality and the relation between them in Twitter

Abstract

In Social Media the directed links formed between the users, are used for the transfer of information. Based on previous research, the rate of information transfer in a social network depends on the strength of connections of the user in the network, which is measured by the centrality value. In this paper, based on data collected from Twitter, we perform an analysis of eigenvector centrality approach of finding the influential users. We investigate the variation in indegree and eigenvector centrality of users participating in a hashtag in Twitter, with respect to change in the amount of interactions. Here interactions are: tweets, mentions and replies. We also investigate the relationship between indegree and eigenvector centrality in a given hashtag. We make the following interesting observations. First, in Twitter, users with high eigenvector centrality need not be influential users. Second, in a given hashtag, there is an increase in users with both high indegree and eigenvector centrality when there are more user interactions. Here interactions are: tweets, mentions and replies, indicating both indegree and eigenvector centrality should be considered when finding influential users. Third, there is a positive correlation between indegree and eigenvector centrality.