We know what @you #tag: does the dual role affect hashtag adoption?
Proceedings of the 21st international conference on World Wide Web
Predicting semantic annotations on the real-time web
Proceedings of the 23rd ACM conference on Hypertext and social media
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In Twitter, users can annotate tweets with hashtags to indicate the ongoing topics. Hashtags provide users a convenient way to categorize tweets. However, two problems remain unsolved during an annotation: (1) Users have no way to know whether some related hashtags have already been created. (2) Users have their own way to categorize tweets. Thus personalization is needed. To address the above problems, we develop a statistical model for Personalized Hashtag Recommendation. With millions of "tweet, hashtag" pairs being generated everyday, we are able to learn the complex mappings from tweets to hashtags with the wisdom of the crowd. Our model considers rich auxiliary information like URLs, locations, social relation, temporal characteristics of hashtag adoption, etc. We show our model successfully outperforms existing methods on real datasets crawled from Twitter.