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        <title>Beyond Binary Labels: Political Ideology Prediction of Twitter Users</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Beyond Binary Labels: Political Ideology Prediction of Twitter Users
%A Preoţiuc-Pietro, Daniel
%A Liu, Ye
%A Hopkins, Daniel
%A Ungar, Lyle
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F preotiuc-pietro-etal-2017-beyond
%X 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.
%R 10.18653/v1/P17-1068
%U https://aclanthology.org/P17-1068
%U https://doi.org/10.18653/v1/P17-1068
%P 729-740

Markdown (Informal)

[Beyond Binary Labels: Political Ideology Prediction of Twitter Users](https://aclanthology.org/P17-1068) (Preoţiuc-Pietro et al., ACL 2017)

• Beyond Binary Labels: Political Ideology Prediction of Twitter Users (Preoţiuc-Pietro et al., ACL 2017)

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.