Who Has the Right to Life: Using Text Mining and Machine Learning to Examine Legal Framing Innovation

Abstract

Since the Supreme Court’s landmark ruling in Roe v. Wade (1973), antiabortion activists have organized efforts to protest the legality of abortion and prevent women from obtaining abortions by physically blocking the entrance to clinics. Although the First Amendment protects the right to protest, antiabortion protesters and blockaders outside of clinics have periodically engaged in behaviors that cause their prochoice opponents to challenge in Court the First Amendment rights of the protesters to protest. My dissertation develops text mining and machine learning techniques for identifying and explaining variation in legal framing in the nine abortion protest Supreme Court cases. In doing so, I how text mining and machine learning can be used to enhance the study of social movement framing as well as the analytic value of social movement theory to understand the results of text mining and machine learning algorithms.