@inproceedings{gupta-etal-2022-examining,
title = "Examining Political Rhetoric with Epistemic Stance Detection",
author = "Gupta, Ankita and
Blodgett, Su Lin and
Gross, Justin and
O{'}connor, Brendan",
editor = "Bamman, David and
Hovy, Dirk and
Jurgens, David and
Keith, Katherine and
O'Connor, Brendan and
Volkova, Svitlana",
booktitle = "Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS)",
month = nov,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.nlpcss-1.11",
doi = "10.18653/v1/2022.nlpcss-1.11",
pages = "89--104",
abstract = "Participants in political discourse employ rhetorical strategies{---}such as hedging, attributions, or denials{---}to display varying degrees of belief commitments to claims proposed by themselves or others. Traditionally, political scientists have studied these epistemic phenomena through labor-intensive manual content analysis. We propose to help automate such work through epistemic stance prediction, drawn from research in computational semantics, to distinguish at the clausal level what is asserted, denied, or only ambivalently suggested by the author or other mentioned entities (belief holders). We first develop a simple RoBERTa-based model for multi-source stance predictions that outperforms more complex state-of-the-art modeling. Then we demonstrate its novel application to political science by conducting a large-scale analysis of the Mass Market Manifestos corpus of U.S. political opinion books, where we characterize trends in cited belief holders{---}respected allies and opposed bogeymen{---}across U.S. political ideologies.",
}
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<abstract>Participants in political discourse employ rhetorical strategies—such as hedging, attributions, or denials—to display varying degrees of belief commitments to claims proposed by themselves or others. Traditionally, political scientists have studied these epistemic phenomena through labor-intensive manual content analysis. We propose to help automate such work through epistemic stance prediction, drawn from research in computational semantics, to distinguish at the clausal level what is asserted, denied, or only ambivalently suggested by the author or other mentioned entities (belief holders). We first develop a simple RoBERTa-based model for multi-source stance predictions that outperforms more complex state-of-the-art modeling. Then we demonstrate its novel application to political science by conducting a large-scale analysis of the Mass Market Manifestos corpus of U.S. political opinion books, where we characterize trends in cited belief holders—respected allies and opposed bogeymen—across U.S. political ideologies.</abstract>
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%0 Conference Proceedings
%T Examining Political Rhetoric with Epistemic Stance Detection
%A Gupta, Ankita
%A Blodgett, Su Lin
%A Gross, Justin
%A O’connor, Brendan
%Y Bamman, David
%Y Hovy, Dirk
%Y Jurgens, David
%Y Keith, Katherine
%Y O’Connor, Brendan
%Y Volkova, Svitlana
%S Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F gupta-etal-2022-examining
%X Participants in political discourse employ rhetorical strategies—such as hedging, attributions, or denials—to display varying degrees of belief commitments to claims proposed by themselves or others. Traditionally, political scientists have studied these epistemic phenomena through labor-intensive manual content analysis. We propose to help automate such work through epistemic stance prediction, drawn from research in computational semantics, to distinguish at the clausal level what is asserted, denied, or only ambivalently suggested by the author or other mentioned entities (belief holders). We first develop a simple RoBERTa-based model for multi-source stance predictions that outperforms more complex state-of-the-art modeling. Then we demonstrate its novel application to political science by conducting a large-scale analysis of the Mass Market Manifestos corpus of U.S. political opinion books, where we characterize trends in cited belief holders—respected allies and opposed bogeymen—across U.S. political ideologies.
%R 10.18653/v1/2022.nlpcss-1.11
%U https://aclanthology.org/2022.nlpcss-1.11
%U https://doi.org/10.18653/v1/2022.nlpcss-1.11
%P 89-104
Markdown (Informal)
[Examining Political Rhetoric with Epistemic Stance Detection](https://aclanthology.org/2022.nlpcss-1.11) (Gupta et al., NLP+CSS 2022)
ACL
- Ankita Gupta, Su Lin Blodgett, Justin Gross, and Brendan O’connor. 2022. Examining Political Rhetoric with Epistemic Stance Detection. In Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS), pages 89–104, Abu Dhabi, UAE. Association for Computational Linguistics.