@InProceedings{johnson-lee-goldwasser:2017:NLPandCSS,
  author    = {Johnson, Kristen  and  Lee, I-Ta  and  Goldwasser, Dan},
  title     = {Ideological Phrase Indicators for Classification of Political Discourse Framing 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     = {90--99},
  abstract  = {Politicians carefully word their statements in order to influence how others
	view an issue, a political strategy called framing. Simultaneously, these
	frames may also reveal the beliefs or positions on an issue of the politician.
	Simple language features such as unigrams, bigrams, and trigrams are important
	indicators for identifying the general frame of a text, for both longer
	congressional speeches and shorter tweets of politicians. However, tweets may
	contain multiple unigrams across different frames which limits the
	effectiveness of this approach. In this paper, we present a joint model which
	uses both linguistic features of tweets and ideological phrase indicators
	extracted from a state-of-the-art embedding-based model to predict the general
	frame of political tweets.},
  url       = {http://www.aclweb.org/anthology/W17-2913}
}