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Social Learning from Tweets to Streets: Exploring Frame alignment in the Romanian #rezist Protests

Europe (Central and Eastern)
Social Movements
Social Media
Protests
Activism
Big Data
Claudiu Vlasie
Babeş-Bolyai University
Sorana Constantinescu
Babeş-Bolyai University
Matthias Hoffmann
Babeş-Bolyai University
Dan Mercea
City, University of London
Felipe G. Santos
City, University of London
Fernando Varga
Babeş-Bolyai University
Claudiu Vlasie
Babeş-Bolyai University
Gabriela Teresa Zenteno Medina
Babeş-Bolyai University

Abstract

Triggered by plans to water down legislation on misconduct in public office, the #rezist demonstrations were a set of rolling protests in Romania, widely described as the most momentous since the fall of communism in 1989. In this paper, we use a dataset of 45,871 #rezist tweets collected for the period between February 2017, at the start of the protests, and the end of 2022. This setting allows us to study the evolution of online communication even after the end of the on-site protests, in 2018. Drawing on social learning and framing theory in social movement studies, we examine the hashtagged communication as a medium not solely for the aggregation and transmission of information but also for a process of frame alignment supported by social facilitation and contextual imitation. The latter two are social learning mechanisms whereby the presence of key individuals who champion one or more frames for the protests (social facilitation) enables alignment, over time, around those frames evidenced through the circulation of similar messages by other people than those key individuals (contextual imitation). First, we expect to find little evidence of alignment before social facilitation. Second, we posit that contextual imitation will facilitate an alignment of frames among on-site and online protesters. Third, we expect alignment to wane once the protests subsided. This study aims to contribute empirically to the understanding of framing processes, in social movements, by showing how social learning can be applied to a process thus far more generically described as frame negotiation.