- Time: 4.00-5.30
- Location: Social Sciences Building, Room 14.33, University of Leeds
Achieving meaningful public engagement can be a difficult endeavour. Once you’ve figured out the topic, format, and target audience, there’s the problem of how the views are actually going to be used and how people may react to the outcomes. This is even harder when you represent a public-facing institution that 47% of the public do not feel they have any influence over. While the UK Parliament uses various ways to engage with the public including citizens assemblies and formal select committee evidence sessions, my research focusses on their online efforts. Facebook is used by 66% of the UK population, and the Parliament has used this channel to pose questions to the public about all aspects of British society. But what happens when thousands of comments are posted over a few days and the MP needs an overview of the main issues straight away?
The aim is then to effectively harness citizen input from large unstructured data generated automatically through digital engagement activities. These digital engagement activities have been popular since they started in 2015 but often attract too many responses for staff to process manually and get a clear picture of what the public is saying. I use machine learning and text mining approaches to analyse the data gathered online to summarise and reveal the network of participant interactions, giving Parliament a more informed idea of who is participating within which social/ideological clusters. Although public engagement is generally viewed as a constructive experience, I will also give examples of situations when it did not go to plan, and how positives can come from this.
Nicole Nisbett is a postgraduate researcher in POLIS, funded by an ESRC White Rose DTP Collaborative Award, working with the House of Commons on the effective harnessing of online Parliamentary engagement data.