Beyond Binary Labels: Political Ideology Prediction of Twitter Users

Daniel Preoţiuc-Pietro, Ye Liu, Daniel Hopkins, Lyle Ungar


Abstract
Automatic political orientation prediction from social media posts has to date proven successful only in distinguishing between publicly declared liberals and conservatives in the US. This study examines users’ political ideology using a seven-point scale which enables us to identify politically moderate and neutral users – groups which are of particular interest to political scientists and pollsters. Using a novel data set with political ideology labels self-reported through surveys, our goal is two-fold: a) to characterize the groups of politically engaged users through language use on Twitter; b) to build a fine-grained model that predicts political ideology of unseen users. Our results identify differences in both political leaning and engagement and the extent to which each group tweets using political keywords. Finally, we demonstrate how to improve ideology prediction accuracy by exploiting the relationships between the user groups.
Anthology ID:
P17-1068
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
729–740
Language:
URL:
https://aclanthology.org/P17-1068
DOI:
10.18653/v1/P17-1068
Bibkey:
Cite (ACL):
Daniel Preoţiuc-Pietro, Ye Liu, Daniel Hopkins, and Lyle Ungar. 2017. Beyond Binary Labels: Political Ideology Prediction of Twitter Users. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 729–740, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Beyond Binary Labels: Political Ideology Prediction of Twitter Users (Preoţiuc-Pietro et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-1068.pdf
Presentation:
 P17-1068.Presentation.pdf
Video:
 https://aclanthology.org/P17-1068.mp4