Visualizing co-retweeting behavior for recommending relevant real-time content

Abstract

Twitter is a popular medium for discussing unfolding events in real-time. Due to the large volume of user generated data during these events, it's important to be able recommend the best content while it's fresh. Current recommendation algorithms for Twitter take into account the user's tweets and her social network, but since real-time events might be unique or unexpected, the history of a user may not be sufficient for finding the most relevant content. Additionally, for users who want to join the conversation at that specific moment (or follow it without having to create an account), the system will be faced with the cold-start problem. We propose a simple visualization technique that considers the activity of the whole community participating in the real-time discussion, by capturing their co-retweeting behavior. Such a technique depicts the big picture, allowing a user to choose content from parts of the community that share her opinions or beliefs.