Ideological Phrase Indicators for Classification of Political Discourse Framing on Twitter

Kristen Johnson, I-Ta Lee, Dan Goldwasser


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.
Anthology ID:
W17-2913
Volume:
Proceedings of the Second Workshop on NLP and Computational Social Science
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venues:
NLP+CSS | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
90–99
Language:
URL:
https://www.aclweb.org/anthology/W17-2913
DOI:
10.18653/v1/W17-2913
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2913.pdf