NavigTweet: A visual tool for influence-based twitter browsing

Abstract

Directed links in social media could represent anything from intimate friendships to common interests. Such directed links determine the flow of information and hence indicate a user’s influence on others—a concept that plays a vital role in sociology and viral marketing. Identifying influencers is an important step towards understanding how information spreads within a network. Social networks follow a power-law degree distribution of nodes, with a few hub nodes and a long tail of peripheral nodes. This paper proposes a novel visual framework to analyze, explore and interact with Twitter ‘Who Follows Who’ relationships, by browsing the friends’ network to identify the key influencers based upon the actual influence of the content they share. We have developed NavigTweet, a novel visualization tool for the influence-based exploration of Twitter network. The core concept of the proposed approach is to identify influencers by browsing through a user’s friends’ network. Then, a power-law based modified force-directed method is applied to clearly display the graph in a multi-layered and multi-clustered way. To gather some insight into the user experience with the pilot release of NavigTweet, we have conducted a qualitative pilot user study. We report on the study and its results, with initial pilot release.