Using Computational Text Mining to Understand Public Priorities for Disability Policy Towards Children in Canadian National Consultations

Using Computational Text Mining to Understand Public Priorities for Disability Policy Towards Children in Canadian National Consultations

Grand Wailea, Hawaii

Identifying policy preferences from public consultations presents a challenge to national and local governments. Computational text mining approaches provide a useful strategy for analyzing the large-scale textual data emerging from these policy processes. In this study, we developed an inductive and deductive text mining approach to understand disability-related policy priorities. This approach is applied to data from the nationwide disability policy consultation conducted in 2016 by the Government of Canada. This process included 18 town hall meetings, 9 thematic roundtables, and online submissions from 92 stakeholders. Transcripts of these consultations were made available to researchers. Three broad research questions were asked of this data, focused on key themes; differences by city size and type of consultation; and impact of two global policy frameworks. The study identified a number of key themes and saw differences by city size. The study identified content related to both the CRPD and CRC.

https://aisel.aisnet.org/hicss-53/cl/text_analytics/5