Big data and democratic speech: Predicting deliberative quality using machine learning techniques

• Published in: Methodological Innovations
• Main Authors: Eleonore Fournier-Tombs, Michael K. MacKenzie
• Format: Article in Journal/Newspaper
• Language: English
• Published: SAGE Publishing 2021
• Subjects: Social Sciences; H; Nunavut; Yukon; Northwest Territories
• Online Access: https://doi.org/10.1177/20597991211010416; https://doaj.org/article/4d4e104a72734d2d9bda52d2785f0889

This article explores techniques for using supervised machine learning to study discourse quality in large datasets. We explain and illustrate the computational techniques that we have developed to facilitate a large-scale study of deliberative quality in Canada’s three northern territories: Yukon, Northwest Territories, and Nunavut. This larger study involves conducting comparative analyses of hundreds of thousands of parliamentary speech acts since the creation of Nunavut 20 years ago. Without computational techniques, we would be unable to conduct such an ambitious and comprehensive analysis of deliberative quality. The purpose of this article is to demonstrate the machine learning techniques that we have developed with the hope that they might be used and improved by other communications scholars who are interested in conducting textual analyses using large datasets. Other possible applications of these techniques might include analyses of campaign speeches, party platforms, legislation, judicial rulings, online comments, newspaper articles, and television or radio commentaries.