Characterizing Microblogs with Topic Models


  • Daniel Ramage Stanford University
  • Susan Dumais Microsoft Research
  • Dan Liebling Microsoft Research



topic models, microblogging, microblog, lda


As microblogging grows in popularity, services like Twitter are coming to support information gathering needs above and beyond their traditional roles as social networks. But most users’ interaction with Twitter is still primarily focused on their social graphs, forcing the often inappropriate conflation of “people I follow” with “stuff I want to read.” We characterize some information needs that the current Twitter interface fails to support, and argue for better representations of content for solving these challenges. We present a scalable implementation of a partially supervised learning model (Labeled LDA) that maps the content of the Twitter feed into dimensions. These dimensions correspond roughly to substance, style, status, and social characteristics of posts. We characterize users and tweets using this model, and present results on two information consumption oriented tasks.




How to Cite

Ramage, D., Dumais, S., & Liebling, D. (2010). Characterizing Microblogs with Topic Models. Proceedings of the International AAAI Conference on Web and Social Media, 4(1), 130-137.