@inproceedings{choi-kim-2025-automated,
title = "Automated Claim{--}Evidence Extraction for Political Discourse Analysis: A Large Language Model Approach to Rodong Sinmun Editorials",
author = "Choi, Gyuri and
Kim, Hansaem",
editor = "Akhtar, Mubashara and
Aly, Rami and
Christodoulopoulos, Christos and
Cocarascu, Oana and
Guo, Zhijiang and
Mittal, Arpit and
Schlichtkrull, Michael and
Thorne, James and
Vlachos, Andreas",
booktitle = "Proceedings of the Eighth Fact Extraction and VERification Workshop (FEVER)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.fever-1.1/",
doi = "10.18653/v1/2025.fever-1.1",
pages = "1--17",
ISBN = "978-1-959429-53-1",
abstract = "This study investigates the feasibility of automating political discourse analysis using large language models (LLMs), with a focus on 87 editorials from Rodong Sinmun, North Korea{'}s official newspaper. We introduce a structured analytical framework that integrates Chain-of-Thought prompting for claim{--}evidence extraction and a GPT-4o{--}based automated evaluation system (G-Eval). Experimental results demonstrate that LLMs possess emerging discourse-level reasoning capabilities, showing notably improved alignment with expert analyses under one-shot prompting conditions. However, the models often reproduced ideological rhetoric uncritically or generated interpretive hallucinations, highlighting the risks of fully automated analysis. To address these issues, we propose a Hybrid Human-in-the-Loop evaluation framework that combines expert judgment with automated scoring. This study presents a novel approach to analyzing politically sensitive texts and offers empirical insights into the quantitative assessment of ideological discourse, underscoring the scalability and potential of automation-driven methodologies."
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%0 Conference Proceedings
%T Automated Claim–Evidence Extraction for Political Discourse Analysis: A Large Language Model Approach to Rodong Sinmun Editorials
%A Choi, Gyuri
%A Kim, Hansaem
%Y Akhtar, Mubashara
%Y Aly, Rami
%Y Christodoulopoulos, Christos
%Y Cocarascu, Oana
%Y Guo, Zhijiang
%Y Mittal, Arpit
%Y Schlichtkrull, Michael
%Y Thorne, James
%Y Vlachos, Andreas
%S Proceedings of the Eighth Fact Extraction and VERification Workshop (FEVER)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 978-1-959429-53-1
%F choi-kim-2025-automated
%X This study investigates the feasibility of automating political discourse analysis using large language models (LLMs), with a focus on 87 editorials from Rodong Sinmun, North Korea’s official newspaper. We introduce a structured analytical framework that integrates Chain-of-Thought prompting for claim–evidence extraction and a GPT-4o–based automated evaluation system (G-Eval). Experimental results demonstrate that LLMs possess emerging discourse-level reasoning capabilities, showing notably improved alignment with expert analyses under one-shot prompting conditions. However, the models often reproduced ideological rhetoric uncritically or generated interpretive hallucinations, highlighting the risks of fully automated analysis. To address these issues, we propose a Hybrid Human-in-the-Loop evaluation framework that combines expert judgment with automated scoring. This study presents a novel approach to analyzing politically sensitive texts and offers empirical insights into the quantitative assessment of ideological discourse, underscoring the scalability and potential of automation-driven methodologies.
%R 10.18653/v1/2025.fever-1.1
%U https://aclanthology.org/2025.fever-1.1/
%U https://doi.org/10.18653/v1/2025.fever-1.1
%P 1-17
Markdown (Informal)
[Automated Claim–Evidence Extraction for Political Discourse Analysis: A Large Language Model Approach to Rodong Sinmun Editorials](https://aclanthology.org/2025.fever-1.1/) (Choi & Kim, FEVER 2025)
ACL