Leveraging tweet ranking in an optimization framework for tweet timeline generation

Abstract

When users search in Twitter, they are overloaded with a mass of microblog posts every time, which are not particularly informative and lack of meaningful organization. Therefore, it is helpful to produce a summarized tweet timeline about the topic. The tweet timeline generation is such a task aiming at selecting a small set of representative tweets to generate meaningful timeline. In this paper, we introduce an optimization framework to jointly model the relevance, novelty and coverage of the tweet timeline, including effective tweet ranking algorithm. Extensive experiments on the public TREC 2014 dataset demonstrate our method can achieve very competitive results against the state-of-art TTG systems.