@InProceedings{tatman-EtAl:2017:NLPandCSS,
  author    = {Tatman, Rachael  and  Stewart, Leo  and  Paullada, Amandalynne  and  Spiro, Emma},
  title     = {Non-lexical Features Encode Political Affiliation on Twitter},
  booktitle = {Proceedings of the Second Workshop on NLP and Computational Social Science},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/W17-2909}
}