Who will tweet more? Finding information feeders in twitter

Abstract

Twitter is an important source of information to users for its giant user group and rapid information diffusion but also made it hard to track topics in oceans of tweets. Such situation points the way to consider the task of finding information feeders, a finer-grained user group than domain experts. Information feeders refer to a crowd of topic tracers that share interests in a certain topic and provide related and follow-up information. In this study, we explore a wide range of features to find Twitter users who will tweet more about the topic after a time-point within a machine learning framework. The features are mainly extracted from the user’s history tweets for that we believe user’s tweet decision depends most on his history activities. We considered four feature families: activeness, timeliness, interaction and user profile. From our results, activeness in user’s history data is most useful. Besides that, we concluded people who gain social influence and make quick response to the topic are more likely to post more topic-related tweets.