Abstract
Social networks and social capital are generally considered to be important variables in explaining the diffusion of behavior. However, it is contested whether the actual social connections, cultural discourse, or individual preferences determine this diffusion. Using discrete choice analysis applied to longitudinal Twitter data, we are able to distinguish between social network influence on one hand and cultural discourse and individual preferences on the other hand. In addition, we present a method using freely available software to estimate the size of the error due to unobserved correlated effects. We show that even in a seemingly saturated model, the log likelihood can increase dramatically by accounting for unobserved correlated effects. Furthermore the estimated coefficients in an uncorrected model can be significantly biased beyond standard error margins.
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Dugundji, E.R., Poorthuis, A., van Meeteren, M. (2013). Capturing Unobserved Correlated Effects in Diffusion in Large Virtual Networks. In: Ghoshal, G., Poncela-Casasnovas, J., Tolksdorf, R. (eds) Complex Networks IV. Studies in Computational Intelligence, vol 476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36844-8_8
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DOI: https://doi.org/10.1007/978-3-642-36844-8_8
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