Sep 23–26, 2019

What does the public say? A computational analysis of regulatory comments

Vlad Eidelman (FiscalNote)
4:35pm5:15pm Wednesday, September 25, 2019
Location: 1E 12/13

Who is this presentation for?

Business Analysts, Data Scientists, NLP experts, Civic hackers, Public Policy




Whether we know it or not, regulations affect our lives every day – from the FCC’s Net Neutrality, to the EPA’s Greenhouse Gas and the SEC’s Dodd-Frank rules. Analysis of the impact these policy changes will have on organizations and the public has long been the domain of legal experts. However, in recent years, as both the ease of participation and interest in rulemaking have grown, there has been an explosion of public participation, and agencies now receive millions of comments each year concerning proposed agency actions.

As these comments are submitted by a wide range of stakeholders, including affected companies, advocacy groups, and the general public, the text within them represents a trove of information about the real-world impact a law will have through a diversity of perspectives and arguments in support and opposition of the proposals.

In this talk, we show how natural language processing and machine learning techniques can be used to automate the comment review process. We present the first large-scale analysis of over 10 million publicly released comments across agencies over the last several decades. By performing automated stance detection and argument mining we can begin to determine which comments are likely more influential than others, and how the expert commenters – businesses and advocacy groups – differ from the general public, and how comments submitted to different agencies differ in what they say and how they say it.

Prerequisite knowledge


What you'll learn

Unstructured legal and policy data is becoming more easily publicly available and contains troves of useful information about how laws will affect companies and people.
Photo of Vlad Eidelman

Vlad Eidelman


Vlad Eidelman is the VP of Research at FiscalNote, where he leads AI R&D into advanced methods for analyzing, modeling, and extracting knowledge from unstructured data related to government, policy, and law and built the first version of the companies patented technology to help organizations predict and act on policy changes. Prior to FiscalNote, he worked as a researcher in a number of academic and industry settings, completing his Ph.D. in CS, as an NSF and NDSEG Fellow, at the University of Maryland and his B.S. in CS and Philosophy at Columbia University. His research focuses on machine learning algorithms for a broad range of natural language processing applications, including entity extraction, machine translation, text classification and information retrieval, especially applied to computational social science. His work has led to 10 patent applications, has been published in conferences like ACL, NAACL and EMNLP, and has been covered by media such as Wired, Vice News, Washington Post and Newsweek.

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