CONSTRUCTING RELATIONAL AND VERIFIABLE PROTEST EVENT DATA: FOUR CHALLENGES AND SOME SOLUTIONS*
Author:
Oliver Pamela1,
Hanna Alex1,
Lim Chaeyoon1
Affiliation:
1. * Pamela Oliver is Professor Emerita of Sociology at the University of Wisconsin – Madison. Alex Hanna is Director of Research at the Distributed AI Research Institute. Chaeyoon Lim is Professor of Sociology at the University of Wisconsin – Madison. Direct correspondence to pamela.oliver@wisc.edu.
Abstract
We call for a relational approach to constructing protest event data from news sources to provide tools for detecting and correcting errors and for capturing the relations among events and between events and the texts describing them. We address two problems with most protest event datasets: (1) inconsistencies and errors in identifying events and (2) disconnect between data structures and what is known about how protests and media accounts of protests are produced. Relational data structures can capture the theoretically important structuring of events into campaigns and episodes and media attention cascades and cycles. Relational data structures support richer theorizing about the interplay of protests and their representations in news media discourses. We present preliminary illustrative data about Black protests from these new procedures to demonstrate the value of this approach.
Publisher
Mobilization Journal
Subject
Sociology and Political Science