Using topical interests and social interactions to identify similar Twitter users

In the present world, social networks such as Twitter have become an important medium for the diffusion of information. Quantifying the connection between the users will help us understand the propagation of the knowledge. With this motivation, we focus on designing and implementing a host of reliable measurements to measure the similarity between two users on the Twitter microblogging platform. These measurements take into account both a user’s topical interests and her social connections. To address the question at hand, we first focused on computing the topical interest-based similarity using user’s historical tweets. We studied various topic modeling approaches and evaluated these approaches in terms of their ability to distinguish the social relationship between Twitter users. We also designed an evaluation approach based on the concept of Homophily in Twitter. To leverage another aspect of content-based similarity, we used hashtags in the tweets to measure users’ interests. Next, we used interactions between the users including retweeting, quoting, replying and mentioning to create a similarity measure to represent the structural aspect. Our experimental results demonstrate that the proposed similarity measurements could significantly distinguish pairs that are socially connected from those which are not connected. As a part of ongoing research, we are studying the role of these similarity measures in modeling the information diffusion process of new TV shows on the Twitter platform.