Capturing Unobserved Correlated Effects in Diffusion in Large Virtual Networks 1 Elenna R. Dugundji, Michiel van Meeteren Ate Poorthuis 2 Universiteit van Amsterdam University of Kentucky 3 Amsterdam, Netherlands Lexington, KY, USA 4 e.r.dugundji,
[email protected] [email protected] 5 Abstract 6 Social networks and social capital are generally considered to be important 7 variables in explaining the diffusion of behavior. However, it is contested 8 whether the actual social connections, cultural discourse, or individual 9 preferences determine this diffusion. Using discrete choice analysis applied 10 to longitudinal Twitter data, we are able to distinguish between social 11 network influence on one hand and cultural discourse and individual 12 preferences on the other hand. In addition, we present a method using freely 13 available software to estimate the size of the error due to unobserved 14 correlated effects. We show that even in a seemingly saturated model, the 15 log likelihood can increase dramatically by accounting for unobserved 16 correlated effects. Furthermore the estimated coefficients in an uncorrected 17 model can be significantly biased beyond standard error margins. 18 19 1 Introduction 20 With the onset of ubiquitous social media technology, people leave numerous traces of their 21 social behavior in – often publicly available – data sets. In this paper we look at a virtual 22 community of independent (“Indie”) software developers for the Macintosh and iPhone that 23 use the social networking site Twitter. Using Twitter's API, we collect longitudinal data on 24 network connections among the Indie developers and their friends and followers 25 (approximately 15,000 nodes) and their use of Twitter client software over a period of five 26 weeks (more than 600,000 “tweets”).