Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2939672.2939697 | KDD |
Keywords | Field | DocType |
Social Good,Government Transparency,Text Reuse,Machine Learning | Legislature,Data mining,Reuse,Computer science,Legislation,Model Legislation,Detector,Gun control | Conference |
Citations | PageRank | References |
1 | 0.35 | 1 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew Burgess | 1 | 62 | 4.13 |
| Eugenia Giraudy | 2 | 1 | 0.69 |
| Julian Katz-Samuels | 3 | 1 | 2.38 |
| Joe Walsh | 4 | 33 | 4.40 |
| Derek Willis | 5 | 2 | 0.76 |
| Lauren Haynes | 6 | 5 | 1.56 |
| Rayid Ghani | 7 | 1142 | 99.45 |