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Replication Data for: Crowd Cohesion and Protest Outcomes

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: Crowd Cohesion and Protest Outcomes
 
Identifier https://doi.org/10.7910/DVN/10VFQX
 
Creator Mueller, Lisa
 
Publisher Harvard Dataverse
 
Description Amidst an unprecedented swell in global protest, scholars and activists wrestle with the question of why protests succeed or fail. I explore a new answer: more cohesive crowds, where protesters agree on their demands, are more likely to win concessions than less cohesive crowds. Drawing on psychology and linguistics, I theorize that cohesive demands are more comprehensible and thus persuasive. I test this theory with a multi-method approach. First, I use cross-national data from 97 protests to estimate the relationship between crowd cohesion and subsequent concessions, applying natural language processing to measure cohesion in participants' self-reported motivations. Second, a survey experiment tests the causal effects of crowd cohesion and assesses comprehensibility of demands as the mechanism driving concessions. Third, case studies of two British protests demonstrate the theory in real-world settings. My findings suggest that activists can improve their odds of success by coordinating around a common goal.
 
Subject Social Sciences
Protests
Social movements
Surveys
Experiments
 
Contributor Mueller, Lisa
 
Source Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, Nazifa Alizada, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Allen Hicken, Garry Hindle, Nina Ilchenko, Joshua Krusell, Anna Lührmann, Seraphine F. Maerz, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Pamela Paxton, Daniel Pemstein, Josefine Pernes, Johannes von R¨omer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundstr¨om, Eitan Tzelgov, Yi-ting Wang, Tore Wig, Steven Wilson and Daniel Ziblatt. 2021. "V-Dem [Country–Year/Country–Date] Dataset v11.1” Varieties of Democracy (V-Dem) Project. https://doi.org/10.23696/vdemds21.