Feature Engineering for US State Legislative Hearings: Stance, Affiliation, Engagement and Absentees

TL;DR

This paper develops and evaluates features for analyzing US state legislative hearings, including legislator engagement, absenteeism, and organizational stance and affiliation, with high accuracy in support detection.

Contribution

It introduces a system for automatically tracking organizational affiliation and stance, and proposes metrics for legislator engagement and absenteeism, applied to California data.

Findings

Affiliation tracking F1 score of 0.872

Support determination F1 score of 0.979

Legislator engagement metrics identified most and least active members

Abstract

In US State government legislatures, most of the activity occurs in committees made up of lawmakers discussing bills. When analyzing, classifying or summarizing these committee proceedings, some important features become broadly interesting. In this paper, we engineer four useful features, two applying to lawmakers (engagement and absence), and two to non-lawmakers (stance and affiliation). We propose a system to automatically track the affiliation of organizations in public comments and whether the organizational representative supports or opposes the bill. The model tracking affiliation achieves an F1 of 0.872 while the support determination has an F1 of 0.979. Additionally, a metric to compute legislator engagement and absenteeism is also proposed and as proof-of-concept, a list of the most and least engaged legislators over one full California legislative session is presented.