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

Author

Listed:
  • Joshua Lilley

    (University of Exeter)

  • Stuart Townley

    (University of Exeter)

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.

Suggested Citation

  • Joshua Lilley & Stuart Townley, 2024. "Tackling transparency in UK politics: application of large language models to clustering and classification of UK parliamentary divisions," Journal of Computational Social Science, Springer, vol. 7(3), pages 2563-2589, December.
    • Handle: RePEc:spr:jcsosc:v:7:y:2024:i:3:d:10.1007_s42001-024-00317-z
    DOI: 10.1007/s42001-024-00317-z
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    References listed on IDEAS

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    1. Benjamin E. Lauderdale & Tom S. Clark, 2014. "Scaling Politically Meaningful Dimensions Using Texts and Votes," American Journal of Political Science, John Wiley & Sons, vol. 58(3), pages 754-771, July.
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    3. Spirling, Arthur & McLean, Iain, 2007. "UK OC OK? Interpreting Optimal Classification Scores for the U.K. House of Commons," Political Analysis, Cambridge University Press, vol. 15(1), pages 85-96, January.
    4. Clinton, Joshua & Jackman, Simon & Rivers, Douglas, 2004. "The Statistical Analysis of Roll Call Data," American Political Science Review, Cambridge University Press, vol. 98(2), pages 355-370, May.
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