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 .

中文翻译：

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在混乱时期向英国政府提交的请愿书的内容、时间和地点

在以政治动荡和不确定性以及日益加剧的分裂和超党派为标志的时代，各国政府需要利用其掌握的一切工具来了解和回应其公民的关切。我们研究了 2015-2017 年英国公众向政府提出的问题（围绕英国脱欧公投），从包含 3050 万个签名的 10,950 份请愿书的数据集中挖掘公众意见。我们使用全新的自然语言处理方法、潜在狄利克雷分配主题建模来提取主要问题。然后我们调查它们的时间动态和地理特征。我们表明，虽然某些问题的受欢迎程度在 2 年内保持稳定，但其他问题受到外部事件的高度影响，例如 2016 年 6 月的公投。我们还研究了请愿书的问题与其签署人所在地理位置之间的关系。我们表明，有些问题得到了全国各地的支持，但其他问题则更为本地化。然后，我们根据选民签署的问题确定六个不同的选区集群。最后，我们通过将请愿书的问题与 Ipsos MORI 调查数据中报告的主要问题进行比较来验证我们的方法。这些结果显示了计算分析请愿书的巨大力量，不仅可以了解公民关心的问题，还可以了解何时何地。然后，我们根据选民签署的问题确定六个不同的选区集群。最后，我们通过将请愿书的问题与 Ipsos MORI 调查数据中报告的主要问题进行比较来验证我们的方法。这些结果显示了计算分析请愿书的巨大力量，不仅可以了解公民关心的问题，还可以了解何时何地。然后，我们根据选民签署的问题确定六个不同的选区集群。最后，我们通过将请愿书的问题与 Ipsos MORI 调查数据中报告的主要问题进行比较来验证我们的方法。这些结果显示了计算分析请愿书的巨大力量，不仅可以了解公民关心的问题，还可以了解何时何地。