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Gossip: Identifying Central Individuals in a Social Network

Nov 2016
Working Paper
Abhijit Banerjee, Arun G. Chandrasekhar, Esther Duflo, Matthew O. Jackson

Is it possible, simply by asking a few members of a community, to identify individuals who are best placed to diffuse information? A model of diffusion shows how members of a community can, just by tracking gossip about others, identify those who are most central in a network according to “diffusion centrality” – a network centrality measure that predicts the diffusion of a piece of information seeded with a network member. Using rich network data from 33 Indian villages, we find that villagers accurately nominate those who are diffusion central – not just those with many friends or in powerful positions. Via a randomized field experiment design to test this theory, in 212 villages we track the diffusion of a piece of information initially given to a small number of “seeds” in each community. Relative to random seeds, seeds nominated by villagers as good “gossips” lead to a 66% increase in the spread of information (from a low basis). The success of the nominees is partly, but not entirely, accounted for by their diffusion centrality.

Publication Keywords: 
Social Learning
Geographic Regions: 
South Asia