Paper: Using Conceptual Class Attributes to Characterize Social Media Users

ACL ID P13-1070
Title Using Conceptual Class Attributes to Characterize Social Media Users
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

We describe a novel approach for automat- ically predicting the hidden demographic properties of social media users. Building on prior work in common-sense knowl- edge acquisition from third-person text, we first learn the distinguishing attributes of certain classes of people. For exam- ple, we learn that people in the Female class tend to have maiden names and en- gagement rings. We then show that this knowledge can be used in the analysis of first-person communication; knowledge of distinguishing attributes allows us to both classify users and to bootstrap new train- ing examples. Our novel approach enables substantial improvements on the widely- studied task of user gender prediction, ob- taining a 20% relative error reduction over the current state-of-the-art.