@inproceedings{loginova-etal-2026-news,
title = "From News Streams to Narrative Intelligence Briefs: {LLM}-Assisted Political Discourse Analysis in the {H}ungarian 2026 Pre-Election Context",
author = "Loginova, Ekaterina and
Ermakov, Maksim and
Khramov, Stephan",
editor = "Afli, Haithem and
Bouamor, Houda and
Zaghouani, Wajdi and
Ghannay, Sahar and
Hossain, Shehenaz",
booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing for Political Sciences ({P}olitical{NLP} 2026)",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.politicalnlp-1.1/",
doi = "10.63317/228tnds8d6kd",
pages = "1--16",
abstract = "Civil-society organisations, journalists, and fact-checkers monitoring elections require scalable ways to convert high-volume political news into actionable narrative intelligence, yet most NLP pipelines stop at classification outputs that are difficult to operationalise. We present a methodology-driven case study assessing whether large language models, constrained by an explicit analytical schema and multi-stage validation, can reliably transform Hungarian pre-election news into structured narrative intelligence briefs. Using RSS-scraped content from 21 Hungarian-language sources (574 election-relevant articles), we implement a multi-stage pipeline: (1) per-article extraction of narrative event frames grounded in the Narrative Policy Framework (actor{--}action{--}target with role assignment and causal claims) and manipulation techniques from the SemEval propaganda taxonomy; (2) embedding-based clustering of narrative frames with domain classification; and (3) constrained brief generation producing five structured sections{---}narrative summary, character map, manipulation profile, escalation assessment, and counter-strategy{---}where counter-strategies are grounded in verified external sources via curated contextual cards and constrained by evidence-based de-escalation principles. We evaluate brief quality through dual-track evaluation combining three human domain experts and three LLM judges on a single brief, with a scaled 29-brief LLM-as-judge assessment, and document key failure modes across a defined taxonomy. We conclude with implications for trustworthy human-in-the-loop political NLP and the practical limits of LLM-assisted narrative intelligence."
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<abstract>Civil-society organisations, journalists, and fact-checkers monitoring elections require scalable ways to convert high-volume political news into actionable narrative intelligence, yet most NLP pipelines stop at classification outputs that are difficult to operationalise. We present a methodology-driven case study assessing whether large language models, constrained by an explicit analytical schema and multi-stage validation, can reliably transform Hungarian pre-election news into structured narrative intelligence briefs. Using RSS-scraped content from 21 Hungarian-language sources (574 election-relevant articles), we implement a multi-stage pipeline: (1) per-article extraction of narrative event frames grounded in the Narrative Policy Framework (actor–action–target with role assignment and causal claims) and manipulation techniques from the SemEval propaganda taxonomy; (2) embedding-based clustering of narrative frames with domain classification; and (3) constrained brief generation producing five structured sections—narrative summary, character map, manipulation profile, escalation assessment, and counter-strategy—where counter-strategies are grounded in verified external sources via curated contextual cards and constrained by evidence-based de-escalation principles. We evaluate brief quality through dual-track evaluation combining three human domain experts and three LLM judges on a single brief, with a scaled 29-brief LLM-as-judge assessment, and document key failure modes across a defined taxonomy. We conclude with implications for trustworthy human-in-the-loop political NLP and the practical limits of LLM-assisted narrative intelligence.</abstract>
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%0 Conference Proceedings
%T From News Streams to Narrative Intelligence Briefs: LLM-Assisted Political Discourse Analysis in the Hungarian 2026 Pre-Election Context
%A Loginova, Ekaterina
%A Ermakov, Maksim
%A Khramov, Stephan
%Y Afli, Haithem
%Y Bouamor, Houda
%Y Zaghouani, Wajdi
%Y Ghannay, Sahar
%Y Hossain, Shehenaz
%S Proceedings of the 3rd Workshop on Natural Language Processing for Political Sciences (PoliticalNLP 2026)
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F loginova-etal-2026-news
%X Civil-society organisations, journalists, and fact-checkers monitoring elections require scalable ways to convert high-volume political news into actionable narrative intelligence, yet most NLP pipelines stop at classification outputs that are difficult to operationalise. We present a methodology-driven case study assessing whether large language models, constrained by an explicit analytical schema and multi-stage validation, can reliably transform Hungarian pre-election news into structured narrative intelligence briefs. Using RSS-scraped content from 21 Hungarian-language sources (574 election-relevant articles), we implement a multi-stage pipeline: (1) per-article extraction of narrative event frames grounded in the Narrative Policy Framework (actor–action–target with role assignment and causal claims) and manipulation techniques from the SemEval propaganda taxonomy; (2) embedding-based clustering of narrative frames with domain classification; and (3) constrained brief generation producing five structured sections—narrative summary, character map, manipulation profile, escalation assessment, and counter-strategy—where counter-strategies are grounded in verified external sources via curated contextual cards and constrained by evidence-based de-escalation principles. We evaluate brief quality through dual-track evaluation combining three human domain experts and three LLM judges on a single brief, with a scaled 29-brief LLM-as-judge assessment, and document key failure modes across a defined taxonomy. We conclude with implications for trustworthy human-in-the-loop political NLP and the practical limits of LLM-assisted narrative intelligence.
%R 10.63317/228tnds8d6kd
%U https://aclanthology.org/2026.politicalnlp-1.1/
%U https://doi.org/10.63317/228tnds8d6kd
%P 1-16
Markdown (Informal)
[From News Streams to Narrative Intelligence Briefs: LLM-Assisted Political Discourse Analysis in the Hungarian 2026 Pre-Election Context](https://aclanthology.org/2026.politicalnlp-1.1/) (Loginova et al., PoliticalNLP 2026)
ACL