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In times marked by political turbulence and uncertainty, as well asincreasing divisiveness and hyperpartisanship, Governments need to use everytool at their disposal to understand and respond to the concerns of theircitizens. We study issues raised by the UK public to the Government during2015-2017 (surrounding the UK EU-membership referendum), mining public opinionfrom a dataset of 10,950 petitions (representing 30.5 million signatures). Weextract the main issues with a ground-up natural language processing (NLP)method, latent Dirichlet allocation (LDA). We then investigate their temporaldynamics and geographic features. We show that whilst the popularity of someissues is stable across the two years, others are highly influenced by externalevents, such as the referendum in June 2016. We also study the relationshipbetween petitions' issues and where their signatories are geographicallylocated. We show that some issues receive support from across the whole countrybut others are far more local. We then identify six distinct clusters ofconstituencies based on the issues which constituents sign. Finally, wevalidate our approach by comparing the petitions' issues with the top issuesreported in Ipsos MORI survey data. These results show the huge power ofcomputationally analyzing petitions to understand not only what issues citizensare concerned about but also when and from where.

What, When and Where of petitions submitted to the UK Government during a time of chaos