Link Prediction and the Evolution of Communities on Twitter

This research is about the influence of link prediction on the evolution of communities on Twitter. We collected tweets from three technology micro-bloggers who led us through their followings and tweets to tens of thousands of unique users over several weeks. We analyzed conventional and alternative information streams for these micro-bloggers based on URLs embedded in their tweets and in tweets of followees and followees-of-followees. We model users based on the most recent URLs embedded on their tweets and the latest users they follow, from which we infer links and extract semantic entities that are indicative of their interests. Furthermore, we propose a pipeline of methods for user modeling and personalization of communities of interest on Twitter. We test the performance of different organizational principles in community design, including the principles of hierarchy, user interests and the baseline follower mechanism on Twitter, which is based on user intuitions. The goal of this thesis is to create a better notion of community by automatically calculating adaptive and personalized structures of followees that produce highly interesting content. Designing communities in this way is use- ful because it enables people to know in which community they are organized during a given period of time and because it enables community-based recommendations. Furthermore, designing communities based on organizational principles enables their automatic construction. Currently, communities are manually constructed by users through a tedious process of following and unfollowing which is based on disconnected user intuitions. We investigate whether it is possible to infer links between Twitter users who are not explicitly connected on Twitter and explore whether such automatically inferred social networks would allow for improving content recommendations on Twitter.Web Information SystemsSoftware TechnologyElectrical Engineering, Mathematics and Computer Scienc