Reconstruction and Analysis of User Context Graphs in Large Social Networks

The goal of the paper is to describe both challenges and solutions for constructing user context graphs in large social networks like Twitter. We first describe data collection process and present straightforward user sampling technique to reduce data size without any loss in performance. Next, we define user political orientation classification task and present 2 types of user context graphs such as: user-follower and user-friend graphs. We claim that in the case of no or limited amount of user data available, user’s context can be used to help predicting different user attributes such as user’s political orientation. We show that by using follower and friend graphs we can improve the performance by 4% using follower and by 5.5% using friend relationships between the users. Finally, we review other works that investigate conversation graphs in Twitter.