Revealing bias when measuring Twitter interaction network structure
Abstract
Network measurement is inherently sensitive to biases, but these biases reveal properties of the network as it affects the measurement. In this paper, we use an approach based on maximum matching to reveal how chain-dense or hub-dense networks are, and apply this to interaction networks generated from the Twitter public stream. We identify how the starting time of the measurements affects our observations, and compare our insights to known properties of Twitter as obtained from a complete sampling. The biases revealed in this study present a step towards understanding how users around the world on Twitter interact with each other.
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