ICWE'12 Proceedings of the 12th international conference on Web Engineering
Discover breaking events with popular hashtags in twitter
Proceedings of the 21st ACM international conference on Information and knowledge management
Event detection using user interaction behavior models
Artificial Intelligence Review
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When micro logging becomes a very popular social media, finding interesting posts from high volume stream of user posts is a challenging research problem. To organize large number of posts, users can assign tags to posts so that these posts can be navigated and searched by tag. In this paper, we focus on modeling the interestingness of hash tags in Twitter, the largest and most active micro logging site. We propose to first construct communities based on both follow links and tagged interactions. We then measure the dispersion and divergence of users and tweets using hash tags among the constructed communities. The interestingness of hash tags are then derived from these community-based dispersion and divergence features. We further introduce a supervised approach to rank hash tags by interestingness. Our experiments on a Twitter dataset show that the proposed approach achieves a fairly good performance.