Demo Data from VLDB 2026 / reviewers in the wild / expert
Derek Willis
dblp:115/9805
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › document processing › document analysis › text reuse detection
plagiarism detection |
0.2 | 1 | 2016 | The Legislative Influence Detector: Finding Text Reuse in State Legislation · KDD 2016 |
Information retrieval › document processing › document analysis
text reuse detection |
0.2 | 1 | 2016 | The Legislative Influence Detector: Finding Text Reuse in State Legislation · KDD 2016 |
Computational social science and digital humanities › legal informatics
legislative text analysis |
0.1 | 1 | 2016 | The Legislative Influence Detector: Finding Text Reuse in State Legislation · KDD 2016 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | The Legislative Influence Detector: Finding Text Reuse in State LegislationabstractState 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. Matthew Burgess, Eugenia Giraudy, Julian Katz-Samuels, Joe Walsh, Derek Willis, Lauren Haynes, Rayid Ghani |
KDD | 5 |