Ideological Phrase Indicators for Classification of Political Discourse Framing on Twitter.

Kristen Johnson     I-Ta Lee     Dan Goldwasser    
Second Workshop on NLP and Computational Social Science (NLP+CSS) collocated with ACL, 2017
[pdf]

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-ofthe-art embedding-based model to predict the general frame of political tweets.


Bib Entry

  @article{JLG_ws_2017,
    author = "Kristen Johnson and I-Ta Lee and Dan Goldwasser",
    title = "Ideological Phrase Indicators for Classification of Political Discourse Framing on Twitter.",
    booktitle = "Second Workshop on NLP and Computational Social Science (NLP+CSS) collocated with ACL",
    year = "2017"
  }