2026/04/14 by Kenneth Benoit, Michael Laver · 2 voices
Computer Science · Social Sciences · #Computational and Text Analysis Methods #Explainable Artificial Intelligence (XAI) #Motivated reasoning #Politics #Salience (neuroscience) #Sentiment Analysis and Opinion Mining #Work (physics)
paper · doi:10.1080/01402382.2026.2637134
published in West European Politics, 1-22 (Taylor & Francis)
openalex publication_date 2026/04/14 · openalex created_date 2026/04/15 · openalex updated_date 2026/06/14
Understanding both party positions on issues and the relative importance, or salience, they attach to these is central to the study of party competition. Recent work shows that large language models (LLMs) can estimate issue positions from manifestos. This article asks whether LLMs can capture issue salience. Building on work by Benoit et al., it adapts LLM-based methods to measure relative issue salience in a multilingual corpus of manifestos. Alternative strategies are tested – including unconstrained scoring, ranking, saliency budgets, and pairwise comparisons – assessing output validity against benchmark expert surveys, human-labelling of manifestos. LLMs produce meaningful estimates of relative issue salience, though with lower correspondence to expert judgements than for issue positions. This highlights the promise of LLMs, the need for careful thinking about concepts of issue importance and salience, and about whether ‘strategic’ party manifestos are the best source of information about the ‘true’ importance for parties of particular issues.