2017
DOI: 10.1177/0038026117710536
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Movement social learning on Twitter: The case of the People’s Assembly

Abstract: This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link Corresponding authorDan Mercea, City, University of London, Northampton Square, EC1V 0HB, London, UK dan.mercea.1@city.ac.uk AbstractThe article examines the U.K. movement People's Assembly against Austerity. It probes the extent to which opposition to austerity expressed on Twitter contributes to building bridges among disparate social groups affected by austerity p… Show more

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Cited by 8 publications
(7 citation statements)
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“…For this study, we used the fast-greedy algorithm introduced by Newman (2004) and Clauset, Newman, and Moore (2004) which is a hierarchical approach for the optimization of modularity in network analysis. This algorithm has already been applied to social network data from Twitter in several studies (e.g., Mercea & Yilmaz, 2018) and has also achieved the best results in the area of community detection based on modularity (Bello-Orgaz, Hernandez-Castro, & Camacho, 2017). The goal of this technique is to optimize the modularity to find community structures in the network.…”
Section: Identification Of Communities and Extraction Of Sub-networkmentioning
confidence: 99%
“…For this study, we used the fast-greedy algorithm introduced by Newman (2004) and Clauset, Newman, and Moore (2004) which is a hierarchical approach for the optimization of modularity in network analysis. This algorithm has already been applied to social network data from Twitter in several studies (e.g., Mercea & Yilmaz, 2018) and has also achieved the best results in the area of community detection based on modularity (Bello-Orgaz, Hernandez-Castro, & Camacho, 2017). The goal of this technique is to optimize the modularity to find community structures in the network.…”
Section: Identification Of Communities and Extraction Of Sub-networkmentioning
confidence: 99%
“…Scholars have not fully addressed the transformative potential of online social movement learning, which Mercea and Yilmaz (2018) characterize as diffuse, “transpiring in the communication over an extended period of substantive and organisational issues, strategy and critical reflections” (p. 20). Scholars have credited offline social movements with providing the “container” for transformative learning, as “without this network, people would not be able to think nor imagine an alternative world” (Westoby & Lyons, 2017, p. 236).…”
Section: Literature Reviewmentioning
confidence: 99%
“…Using Hoggan’s (2016b) typologies of transformative learning theory to understand the learning in an emerging social movement, this article speaks to how social movement learning occurs in online spaces and expands on the notion that transformative learning is “a social exercise that occurs in everyday settings” (Westoby & Lyons, 2017, p. 228)—even everyday online settings. Following Mercea and Yilmaz’s (2018) lead, we seek “to deepen the study of learning in social movements” (p. 20), by bridging literature on social media activist groups, social movement learning, and transformational learning theory—bodies of literature we have yet to see merged. We argue that the transformative learning we identify in post-2016 online resistance groups adds nuance to the scholarly conversation that all too often views online organizing as either ineffective or short-lived and offers insight into the educative potential of the medium.…”
mentioning
confidence: 99%
“…In organizational studies, the relevance and emergence of understanding the topic are seen in full theoretical reflections and recent literature reviews that address algorithmically mediated management and its potential impacts (Kellogg et al, 2020;Trittin-Ulbrich et al, 2020;Brevini & Pasquale, 2020). Among the consequences discussed, studies related to algorithms and organizations reveal the possibility that data management interferes in various aspects of organizational dynamics, such as the formation and relationships of social groups in the organizational environment (Vaast, 2020;Lage & Rodrigues, 2020), fantasies of digitization in the workplace (Hensmans, 2020), loss of privacy (Anteby & Chan, 2018;Rosenblat & Stark, 2016;Woodcock, 2020); algorithmic control of work Curchod et al, 2019;Faraj et al, 2018), and workers' habits (Elmholdt et al, 2020) such as algo activity or workers' activism movements (Kellogg et al, 2020;Mercea & Yilmaz, 2018;Petriglieri et al, 2019;Mercea & Yilmaz, 2018;Etter & Albu, 2020;Birch, 2020;Petriglieri et al, 2019).…”
Section: Introductionmentioning
confidence: 99%