Structural holes of “Anti-Maidan” Facebook page.

Ukrainian revolution “Euromaidan” triggered a quick response known as “Anti-Maidan” social movement. This movement has a Facebook page which I analyse using Netvizz. Here I discuss an undirected network of people that are tied by commenting to the same posts during March and April 2014. Overall, I have 596 nodes and 7,204 edges. A brilliant paper recently published in Social Networks shows thatbetweenness centrality has no effect on making profitable choices in a lab. I wonder if betweenness centrality does not play any role in my data as well? When a person makes a comment in my data he or she can receive a premium from others for their opinion (Facebook does not allow punishments though). In a way, making more likes (profit) depends on the correct opinion and choice of words. In this setting closeness centrality means that a person is directly connected to many other threads of conversations. However, the same person may be well connected within one cluster only, making the exchange of information redundant. Betweenness must solve this issue. Indeed, an OLS model shows that 40% of variation in likes is explained by betweenness, whereas closeness has no significant effect. PS. and of course an image made in Gephi =)

UPD: of course, the original article of Bas is about social learning, and here we don’t have any empirical sign of it

UPantimaidan(march-april)

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