Characterizing Microblogs with Topic Models

TLDR

Microblogging platforms such as Twitter are evolving beyond social networking to support information gathering, with content dimensions reflecting substance, style, status, and social characteristics. The study seeks to identify unmet information needs on Twitter and proposes better content representations to address these challenges. A scalable partially supervised Labeled LDA model is implemented to map tweet content into the identified dimensions. Applying this model, the authors characterize users and tweets and report results on two information‑consumption tasks.

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 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.