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#ContextMatters: Advantages and Limitations of Using Machine Learning to Support Women in Politics by Comer, Jacqueline; Work, Sam; Mathewson, Kory W; Cuthbertson, Lana; Machin, Kasey is a scholarly article available to read on EtoBox.

What is #ContextMatters: Advantages and Limitations of Using Machine Learning to Support Women in Politics about?

The United Nations identified gender equality as a Sustainable Development Goal in 2015, recognizing the underrepresentation of women in politics as a specific barrier to achieving gender equality. Political systems around the world experience gender inequality across all levels of elected government as fewer women run for office than men. This is due in part to online abuse, particularly on social media platforms like Twitter, where women seeking or in power tend to be targeted with more toxic maltreatment than their male counterparts. In this paper, we present reflections on ParityBOT - the first natural language processing-based intervention designed to affect online discourse for women in politics for the better, at scale. Deployed across elections in Canada, the United States and New Zealand, ParityBOT was used to analyse and classify more than 12 million tweets directed at women candidates and counter toxic tweets with supportive ones. From these elections we present three case studies highlighting the current limitations of, and future research and application opportunities for, using a natural language processing-based system to detect online toxicity, specifically with reg

Author

Comer, Jacqueline; Work, Sam; Mathewson, Kory W; Cuthbertson, Lana; Machin, Kasey

Published

2021

Language

EN