Assessing the prominence of interest groups in parliament: a supervised machine learning approach

Ascertaining which interest groups are considered relevant by policymakers presents an important challenge for political scientists. Existing approaches often focus on the submission of written evidence or the inclusion in expert committees. While these approaches capture the effort of groups, they do not directly indicate whether policy makers consider these groups as highly relevant political actors. In this paper we introduce a novel theoretical approach to address this important question, namely prominence. We argue that, in the legislative arena, prominence can be operationalised as groups being mentioned strategically – used as a resource – by elected officials as they debate policy matters. Furthermore, we apply a machine learning solution to reliably assess which groups are prominent among legislators. We illustrate this novel method relying on a dataset of mentions of over 1300 national interest groups in parliamentary debates in Australia over a six-year period (2010–2016)