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Mobilizing Online Data to Understand Offline Mobilization: Two Attempts at Online Observational Research in Russia
Samuel A. Greene, King´s College London
Conference Paper
May 11, 2013

This presentation reviews the strengths and weaknesses of two recent attempts to use data collected from online social media to understand the structures and agents of the recent protest wave in Russia. Beginning in December 2011 and lasting, in its active phase, for approximately six months, Russia’s recent protest wave surprised politicians, participants and analysts alike, none of whom saw it coming and few of whom could predict its outcome. Events on the Russian street challenged many prior notions about the nature of public opinion, the social contract and mobilizational structures in Russia, forcing political scientists and sociologists to go back to the drawing board. Online social media were demonstrably important in the organization and coverage of the protest events, but they also provided an opportunity for researchers to observe certain kinds of behavior and social interaction directly. Two such attempts were undertaken by the author: In the first, data from Twitter were used to identify structures of communication, and to ask how the medium was used by various proponents and opponents of the movement. In the second, data from Facebook were used to identify protest-related communities, and to ask where new protest constituencies came from. In both cases, a secondary motive was to understand how the use of Twitter and Facebook data might illuminate offline phenomena. Both projects did lead to useful, and in some ways counterintuitive, empirical results, finding an unexpectedly powerful role for professional journalists in framing the movement, and undermining earlier assumptions about the mobilization of a previously apolitical ‘hipster’ constituency. But the limitations of the technology and the opportunism of the methods raise serious questions about the degree to which these findings and their broader theoretical implications may ‘travel’, both across borders and over time.
Full paper and presentation here

 
 
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