mlsanityData to 14 Oct 2026

Elite Polarization in European Parliamentary Speeches: a Novel Measurement Approach Using Large Language Models

arXiv 2507.06658 ↗First posted 9 Jul 2025cs.CL cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Theories of democratic stability, populism, and party-system crisis often point to a form of polarization that comparative research rarely measures directly: hostile relations among political elites. Existing comparative measures capture adjacent phenomena, including mass affective polarization, or elite ideological distance, but not directed mutual elite evaluation. This paper introduces the Elite Polarization Score, a measurement of out-party evaluations in parliamentary speech. Large Language Models identify political actors mentioned in parliamentary debates, recover speaker-target pairs, estimate the sentiment directed at each actor, standardize heterogeneous references into party dyads, and aggregate these evaluations into party- and parliament-level measures of mutual out-party negativity. The validity of the approach is demonstrated on parliamentary corpora from the United Kingdom, Hungary, and Italy, covering up to four decades of debate. The resulting measure is conceptually distinct from mass affective polarization, elite ideological polarization, incivility, negative campaigning, and general sentiment. Evidence from the UK case study shows that it is also empirically distinct from mass affective polarization, elite ideological polarization, and incivility. Extreme negative evaluations can also be used to locate pernicious polarization rhetoric. Validation across three countries finds no false discoveries, sentiment estimates accurate to roughly 10 percent of the scale range, and AI sensitivity that meets or exceeds that of human coders in two of three settings. Because the algorithm is multilingual, requires no task-specific training, and can be aggregated by party and quarter, it provides a scalable basis for future cross-national research on what produces elite polarization and what elite polarization itself produces

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 68 references that are in the map. First posted on arXiv 9 Jul 2025.

Builds on

8 of 68 references.

References · 8

  1. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  2. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,411
  3. 2023ChatGPT Outperforms Crowd-Workers for Text-Annotation Taskscited by 870
  4. 2018A Survey on Deep Learning for Named Entity Recognitioncited by 282
  5. 2024LLMs Beyond English: Scaling the Multilingual Capability of LLMs with Cross-Lingual Feedbackcited by 51
  6. 2023SentimentGPT: Exploiting GPT for Advanced Sentiment Analysis and its Departure from Current Machine Learningcited by 38
  7. 2019Sentiment and position-taking analysis of parliamentary debates: A systematic literature reviewcited by 13
  8. 2023Positioning Political Texts with Large Language Models by Asking and Averagingcited by 13

Built on by

Not cited yet.