CONSTRUCTING RELATIONAL AND VERIFIABLE PROTEST EVENT DATA: FOUR CHALLENGES AND SOME SOLUTIONS*
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. Preprint of a paper that has been accepted for publication in Mobilization: An International Quarterly. The published version will be edited to meet publication formatting guidelines. This paper is a substantial revision of “Constructing Theory-Informed Relational and Verifiable Protest Event Data” posted to SocArXiv https://osf.io/preprints/socarxiv/spzgx/ in September 2021.
Cite this
Pamela Elaine Oliver, Alex Hanna, Chaeyoon Lim (2023). CONSTRUCTING RELATIONAL AND VERIFIABLE PROTEST EVENT DATA: FOUR CHALLENGES AND SOME SOLUTIONS*. Mobilization An International Quarterly. https://doi.org/10.17813/1086-671x-28-1-1
@article{elaine2023constructing,
title = {CONSTRUCTING RELATIONAL AND VERIFIABLE PROTEST EVENT DATA: FOUR CHALLENGES AND SOME SOLUTIONS*},
author = {Oliver, Pamela Elaine and Hanna, Alex and Lim, Chaeyoon},
year = {2023},
journal = {Mobilization An International Quarterly},
doi = {10.17813/1086-671x-28-1-1}
}Citations in this library
Stored only when both papers are in the corpus — 15 citations exist upstream.