In many citizen participation projects, the main challenge isn’t to gather citizen input: it’s to analyse it. In recent years, social media and new technologies have made it easier than ever for governments and organisations to reach out to citizens and collect their input on any given topic. As the amount of collected information grows, it is however becoming increasingly difficult to process this data, meaning that useful insights are getting lost in the process.
At CitizenLab, we believe that helping administration and civil society groups process and analyse citizen contributions is an essential part of citizen engagement projects. Easing up analysis would not only save governments both time and money: it would help them tap into the collective intelligence of their constituents and make better decisions. This, in turn, could increase trust in public decision-making and encourage dialogue between governments and their citizens.
In order to make this a reality, CitizenLab has developed its own Natural Language Processing (NLP) techniques. This technology, based on machine learning and artificial intelligence, analyses large amounts of unstructured citizen input (ideas, comments, votes) and quickly extracts the main insights.
Ideas are automatically classified, grouped together or geo-referenced. Administrators of the platform can see at a glance what topics citizens are discussing, how the topics differ across different demographic groups, and how conversations are located around the town or region. It could be that in a neighbourhood, older citizens are asking for better roads whilst their younger counterparts want more public transportation. With reliable data at their fingertips, policy-makers are better equipped to make decisions and to design policies that truly respond to their citizens’ needs. Of course, the technology is also applicable beyond government: it also helps empower citizen groups who use our platform, and help them carry their ideas even when they don’t have a solid organisational structure or large resources.
CitizenLab has recently applied its NLP technology to the YouthForClimate contributions. YouthForClimate is a movement that sparked off in Sweden, and has now gone global. Young people across the world have been protesting in the streets and demanding more actions against climate change. Y4C climate took off in Belgium in late 2018. As press attention and momentum grew, the organisers quickly decided they needed a way to channel the energy being expressed every week in the street. A CitizenLab platform was set up to gather citizen contributions, where users from Belgium and beyond were invited to submit ideas on how to tackle climate change. The platform quickly became a place for lively discussion: in just under 3 months, users posted over 1,700 ideas, 2,600 comments and voted over 32,000 times for the initiatives they wanted to support.
This is where the challenge truly started. Collecting input was just the easy part: in order to turn these ideas into meaningful actions and recommendations, YouthForClimate needed to process thousands of ideas in a short period of time. The organisers – high school students with little to no previous experience in leading a movement of this size – lacked both the time and the technical skills to analyse this data. By using the data analysis technology, CitizenLab helped the organisers turn over 1,700 ideas into 15 concrete priorities for climate. We first read through the top ideas to determine themes, and then automatically applied these themes to ideas in over three languages (see the full case study here: https://www.citizenlab.co/blog/civic-engagement/youth-for-climate-case-study/).
Digital platforms can give citizens a voice, and encourage them to engage with their governments. Applying artificial intelligence to citizen participation allows these projects to then come full circle by allowing governments or civil society groups to process citizens’ input more efficiently and at a lesser cost. By having access to better data, they are able to make better decisions. This, in turn, increases trust and support for the policies that are implemented and strengthens democracy in the long run.