  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 dataset of 10,950 petitions (representing 30.5 million signatures). We
extract the main issues with a ground-up natural language processing (NLP)
method, latent Dirichlet allocation (LDA). We then investigate their temporal
dynamics and geographic features. We show that whilst the popularity of some
issues is stable across the two 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 analyzing petitions to understand not only what issues citizens
are concerned about but also when and from where.
