Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2012

Abstract

Twitter network is currently overwhelmed by massive amount of tweets generated by its users. To effectively organize and search tweets, users have to depend on appropriate hashtags inserted into tweets. We begin our research on hashtags by first analyzing a Twitter dataset generated by more than 150,000 Singapore users over a three-month period. Among several interesting findings about hashtag usage by this user community, we have found a consistent and significant use of new hashtags on a daily basis. This suggests that most hashtags have very short life span. We further propose a novel hashtag recommendation method based on collaborative filtering and the method recommends hashtags found in the previous month's data. Our method considers both user preferences and tweet content in selecting hashtags to be recommended. Our experiments show that our method yields better performance than recommendation based only on tweet content, even by considering the hashtags adopted by a small number (1 to 3)of users who share similar user preferences.

Keywords

Twitter, Hashtag, Recommendation systems

Discipline

Communication Technology and New Media | Databases and Information Systems | Social Media

Research Areas

Data Science and Engineering

Publication

Social Informatics: 4th International Conference, SocInfo 2012, Lausanne, Switzerland, December 5-7, 2012. Proceedings

Volume

7710

First Page

337

Last Page

350

ISBN

9783642353864

Identifier

10.1007/978-3-642-35386-4_25

Publisher

Springer

City or Country

Cham

Copyright Owner and License

Authors/LARC

Additional URL

http://doi.org/10.1007/978-3-642-35386-4_25

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