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This paper examines the impact of civic participation on a crowdsourced policymaking process by using a novel data analytics method, Natural Language Processing (NLP). Using data from a crowdsourced urban planning process in the City of Palo Alto in California, we examined the impact of civic input on the city’s Comprehensive City Plan update. Our findings revealed big differences between the crowdsourced input, the representatives’ input, and the final policy. The differences reflect three agendas from the crowd, representatives, and bureaucrats. Although crowdsourcing aims to combine the three agendas to extract epistemic value in civic engagement, the civic data overload constrains the government and representatives’ ability to process and consider crowdsourced data. This paradox of asking for civic participation but not considering it in the policymaking process poses serious challenges to participatory democracy—a challenge to which NLP can provide at least a partial solution by automating data analysis.
Kaiping Chen, Stanford University
Tanja Katarina Aitamurto, Stanford
Ahmed Cherif, U of California - Berkeley