Tackling transparency in UK politics: application of large language models to clustering and classification of UK parliamentary divisions

Joshua Lilley () and Stuart Townley ()
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Joshua Lilley: University of Exeter
Stuart Townley: University of Exeter

Journal of Computational Social Science, 2024, vol. 7, issue 3, No 12, 2563-2589

Abstract: Abstract For a healthier democracy in the UK, novel methods of visualising political data are key to improving transparency, and encouraging engagement. The paper proposes a visualisation tool, using Large language models (LLMs), such as GPT3.5 and GPT4, to conduct Natural Language Processing (NLP) in a novel methodology. We investigate partisan voting profiles, specifically of the Conservative, Labour, and Liberal Democrat parties along 11 predetermined dimensions, ranging from Immigration and Borders, over Welfare and Social Housing, to European Union and Foreign Affairs. Higher order dimensions reveals shifts in party preference over time, while clear trends of more extreme voting behaviour can be seen across parties between 2016 and 2023. The novel visualisation methodology reveals that voting behaviour has become more polarised along party lines, with Labour becoming more left-wing and Conservatives becoming more right-wing regarding most political topics. Liberal Democrats voting behaviour has typically been those of an opposition party, albeit becoming somewhat more extreme.

Keywords: Large language models; Natural language processing; Digital democracy; Visualisation tools; GPT4 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s42001-024-00317-z

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