Year: 2022
Type: conference-paper
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 RoBERTabased model for multi-source stance predictions that outperforms more complex state-ofthe-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.
Authors: Ankita Gupta, Su Lin Blodgett, Justin H Gross, Brendan O’Connor
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: National Science Foundation
OpenAlex ID: W4385573996