User oriented tweet ranking: a filtering approach to microblogs

The increasing volume of streaming data on microblogs has re-introduced the necessity of effective filtering mechanisms for such media. Microblog users are overwhelmed with mostly uninteresting pieces of text in order to access information of value. In this paper, we propose a personalized tweet ranking method, leveraging the use of retweet behavior, to bring more important tweets forward. In addition, we also investigate how to determine the audience of tweets more effectively, by ranking the users based on their likelihood of retweeting the tweets. Finally, conducting a pilot user study, we analyze how retweet likelihood correlates with the interestingness of the tweets.