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What, when and where of petitions submitted to the UK government during a time of chaos

Author

Listed:
  • Bertie Vidgen

    (University of Oxford
    Alan Turing Institute)

  • Taha Yasseri

    () (University of Oxford
    Alan Turing Institute)

Abstract

In times marked by political turbulence and uncertainty, as well as increasing divisiveness and hyperpartisanship, Governments need to use every tool at their disposal to understand and respond to the concerns of their citizens. We study issues raised by the UK public to the Government during 2015–2017 (surrounding the UK EU membership referendum), mining public opinion from a data set of 10,950 petitions, which contain 30.5 million signatures. We extract the main issues with a ground-up natural language processing method, latent Dirichlet allocation topic modelling. We then investigate their temporal dynamics and geographic features. We show that whilst the popularity of some issues is stable across the 2 years, others are highly influenced by external events, such as the referendum in June 2016. We also study the relationship between petitions’ issues and where their signatories are geographically located. We show that some issues receive support from across the whole country, but others are far more local. We then identify six distinct clusters of constituencies based on the issues which constituents sign. Finally, we validate our approach by comparing the petitions’ issues with the top issues reported in Ipsos MORI survey data. These results show the huge power of computationally analysing petitions to understand not only what issues citizens are concerned about but also when and from where.

Suggested Citation

  • Bertie Vidgen & Taha Yasseri, 2020. "What, when and where of petitions submitted to the UK government during a time of chaos," Policy Sciences, Springer;Society of Policy Sciences, vol. 53(3), pages 535-557, September.
  • Handle: RePEc:kap:policy:v:53:y:2020:i:3:d:10.1007_s11077-020-09395-y
    DOI: 10.1007/s11077-020-09395-y
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    References listed on IDEAS

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    1. Lucas Böttcher & Olivia Woolley-Meza & Dirk Brockmann, 2017. "Temporal dynamics of online petitions," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-12, May.
    2. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    3. Michael Howlett, 2009. "Governance modes, policy regimes and operational plans: A multi-level nested model of policy instrument choice and policy design," Policy Sciences, Springer;Society of Policy Sciences, vol. 42(1), pages 73-89, February.
    4. Araz Taeihagh, 2017. "Crowdsourcing: a new tool for policy-making?," Policy Sciences, Springer;Society of Policy Sciences, vol. 50(4), pages 629-647, December.
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