Home > Events > CLIP Colloquium: Vlad Eidelman (FiscalNote)
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CLIP Colloquium: Vlad Eidelman (FiscalNote)

Time: 
Wednesday, February 08, 2017 - 11:00 AM to 12:00 PM
Location: 
3258 A.V. Williams Building

 

Title: Recognizing Stance and Influence from Public Comments in Regulatory Rule-making

Abstract: Administrative agencies in the United States, such as the FCC, EPA, and SEC, have the responsibility of implementing laws through the promulgation of regulations. In recent years, as both the ease of participation and interest in rule-making have grown, there has been an explosion of public participation, and agencies now receive millions of comments from the public each year concerning proposed agency actions. These comments are submitted by a wide range of stakeholders, including affected companies, advocacy groups, and interested individuals, and include a diversity of perspectives, viewpoints, and arguments in support and opposition of the proposals. Regulatory agencies are then required to review and respond to these comments, in a painstakingly manual manner, as part of the notice-and-comment process.

This talk presents the first large-scale analysis of organizational and individual stance using 10 million comments submitted across all regulatory agencies over the last several decades. We automate the review process by constructing multi-aspect stance detection and sentiment analysis models and examine their ability to assess the commenters disposition to the proposed rules. We further investigate discriminating influential comments from less substantive ones, and how different organizations approach the process.

Bio: Vlad Eidelman is the VP of Research at FiscalNote, where he leads the Data Science team and is responsible for guiding R&D into advanced methods across the company. 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, with Philip Resnik at the University of Maryland and his B.S. in CS and Philosophy at Columbia University. His research focuses on machine learning for natural language processing applications in computational social science.