A Comparative Study of Citizen Crowdsourcing Platforms and the Use of Natural Language Processing (NLP) for Effective Participatory Democracy

The use of crowdsourcing platforms to harness citizen insights for policymaking has gained increasing importance in regional and national policy planning. Participatory democracy using crowdsourcing platforms includes various initiatives, such as generating ideas for new law reforms (Aitamurto and Landemore 2015], economic development, and solving challenges related to how to create inclusive social actions and interventions for better, healthier, and more prosperous local communities (Bentley and Pugalis, 2014). Such case observations, coupled with the increasing prevalence of internet-based communication, point to the real benefits of implementing participatory democracies on a mass scale in which citizens are invited to contribute their ideas, opinions, and deliberations (Salganik and Levy 2015). By adopting collective intelligence platforms, public authorities can harness local knowledge from citizens to find the right ‘policy mix’ and collaborate with citizens and relevant actors in the policymaking processes. This comparative study aims to validate the adoption of collective intelligence and artificial intelligence/natural language processing (NLP) on crowdsourcing platforms for effective participatory democracy and policymaking in local governments. The study compares 15 citizen crowdsourcing platforms, including Natural language Processing (NLP), for policymaking across Europe and the United States. The study offers a framework for working with citizen crowdsourcing platforms and exploring the usefulness of NLP on the platforms for effective participatory democracy