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The Legislative Influence Detector: Finding Text Reuse in State Legislation

Published: 13 August 2016 Publication History

Abstract

State legislatures introduce at least 45,000 bills each year. However, we lack a clear understanding of who is actually writing those bills. As legislators often lack the time and staff to draft each bill, they frequently copy text written by other states or interest groups. However, existing approaches to detect text reuse are slow, biased, and incomplete. Journalists or researchers who want to know where a particular bill originated must perform a largely manual search. Watchdog organizations even hire armies of volunteers to monitor legislation for matches. Given the time-consuming nature of the analysis, journalists and researchers tend to limit their analysis to a subset of topics (e.g. abortion or gun control) or a few interest groups.
This paper presents the Legislative Influence Detector (LID). LID uses the Smith-Waterman local alignment algorithm to detect sequences of text that occur in model legislation and state bills. As it is computationally too expensive to run this algorithm on a large corpus of data, we use a search engine built using Elasticsearch to limit the number of comparisons. We show how system has found 45,405 instances of bill-to-bill text reuse and 14,137 instances of model-legislation-to-bill text reuse. System reduces the time it takes to manually find text reuse from days to seconds.

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      cover image ACM Conferences
      KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
      August 2016
      2176 pages
      ISBN:9781450342322
      DOI:10.1145/2939672
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      Published: 13 August 2016

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      1. government transparency
      2. social good

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      • (2022)Trends and Opportunities for Bridging Prevention Science and US Federal PolicyPrevention Science10.1007/s11121-022-01403-2Online publication date: 5-Aug-2022
      • (2021)Interest group lobbying and partisan polarization in the United States: 1999–2016Political Science Research and Methods10.1017/psrm.2021.510:3(488-506)Online publication date: 22-Feb-2021
      • (2021)Representing Standard Text Formulations as Directed GraphsDocument Analysis and Recognition – ICDAR 2021 Workshops10.1007/978-3-030-86159-9_34(475-487)Online publication date: 2-Sep-2021
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      • (2020)News Provenance: Revealing News Text Reuse at Web-Scale in an Augmented News Search ExperienceExtended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3334480.3375225(1-8)Online publication date: 25-Apr-2020
      • (2020)Exploring information exchange among interest groups: a text-reuse approachJournal of European Public Policy10.1080/13501763.2020.181713227:11(1698-1717)Online publication date: 17-Nov-2020
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      • (2018)Formal Authority, Persuasive Power, and Effectiveness in State LegislaturesState Politics & Policy Quarterly10.1177/153244001878673018:3(324-346)Online publication date: 21-Jul-2018
      • (2018)Sustained Organizational Influence: American Legislative Exchange Council and the Diffusion of Anti‐Sanctuary PolicyPolicy Studies Journal10.1111/psj.1228447:3(735-773)Online publication date: 28-Nov-2018
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