@inproceedings{tatman-etal-2017-non,
title = "Non-lexical Features Encode Political Affiliation on {T}witter",
author = "Tatman, Rachael and
Stewart, Leo and
Paullada, Amandalynne and
Spiro, Emma",
editor = {Hovy, Dirk and
Volkova, Svitlana and
Bamman, David and
Jurgens, David and
O{'}Connor, Brendan and
Tsur, Oren and
Do{\u{g}}ru{\"o}z, A. Seza},
booktitle = "Proceedings of the Second Workshop on {NLP} and Computational Social Science",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2909",
doi = "10.18653/v1/W17-2909",
pages = "63--67",
abstract = "Previous work on classifying Twitter users{'} political alignment has mainly focused on lexical and social network features. This study provides evidence that political affiliation is also reflected in features which have been previously overlooked: users{'} discourse patterns (proportion of Tweets that are retweets or replies) and their rate of use of capitalization and punctuation. We find robust differences between politically left- and right-leaning communities with respect to these discourse and sub-lexical features, although they are not enough to train a high-accuracy classifier.",
}
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%0 Conference Proceedings
%T Non-lexical Features Encode Political Affiliation on Twitter
%A Tatman, Rachael
%A Stewart, Leo
%A Paullada, Amandalynne
%A Spiro, Emma
%Y Hovy, Dirk
%Y Volkova, Svitlana
%Y Bamman, David
%Y Jurgens, David
%Y O’Connor, Brendan
%Y Tsur, Oren
%Y Doğruöz, A. Seza
%S Proceedings of the Second Workshop on NLP and Computational Social Science
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F tatman-etal-2017-non
%X Previous work on classifying Twitter users’ political alignment has mainly focused on lexical and social network features. This study provides evidence that political affiliation is also reflected in features which have been previously overlooked: users’ discourse patterns (proportion of Tweets that are retweets or replies) and their rate of use of capitalization and punctuation. We find robust differences between politically left- and right-leaning communities with respect to these discourse and sub-lexical features, although they are not enough to train a high-accuracy classifier.
%R 10.18653/v1/W17-2909
%U https://aclanthology.org/W17-2909
%U https://doi.org/10.18653/v1/W17-2909
%P 63-67
Markdown (Informal)
[Non-lexical Features Encode Political Affiliation on Twitter](https://aclanthology.org/W17-2909) (Tatman et al., NLP+CSS 2017)
ACL