Characterizing microblogs with topic models

Abstract

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 user intract ion with Twitter is still primarily focused on their social graphs, forcing the often in appropriate 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. Copyright © 2010, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.