@inproceedings{johnson-etal-2017-ideological,
title = "Ideological Phrase Indicators for Classification of Political Discourse Framing on {T}witter",
author = "Johnson, Kristen and
Lee, I-Ta and
Goldwasser, Dan",
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/W17-2913",
doi = "10.18653/v1/W17-2913",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Ideological Phrase Indicators for Classification of Political Discourse Framing on Twitter
%A Johnson, Kristen
%A Lee, I-Ta
%A Goldwasser, Dan
%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 johnson-etal-2017-ideological
%X 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.
%R 10.18653/v1/W17-2913
%U https://aclanthology.org/W17-2913
%U https://doi.org/10.18653/v1/W17-2913
%P 90-99
Markdown (Informal)
[Ideological Phrase Indicators for Classification of Political Discourse Framing on Twitter](https://aclanthology.org/W17-2913) (Johnson et al., NLP+CSS 2017)
ACL