From News Streams to Narrative Intelligence Briefs: LLM-Assisted Political Discourse Analysis in the Hungarian 2026 Pre-Election Context

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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