Given that human activities increasingly are mediated by or take place in online environments, we are afforded with novel opportunities for understanding how collective, population-level phenomena and outcomes can be related to individual behaviours. My talk considers a family of simple generative models to represent the collective behaviour of millions of social networking site users who make choices between different software applications that they can install. The proposed models incorporate two distinct social mechanisms: (1) imitative behaviour reflecting the influence of recent installation activities of other users; (2) rich-get-richer popularity dynamics where users are influenced by the cumulative popularity of each application. Interestingly, although various combinations of the two mechanisms yield long-time behaviour that is consistent with data, the only models that reproduce the observed temporal dynamics well are those that strongly emphasize the recent installation activities of other users over their cumulative popularity. Hence social imitation seems to be especially important in this information rich environment. Methodologically, our work demonstrates that even when using purely observational data, as opposed to experimental research designs, temporal data-driven modelling can in fact under some circumstances effectively distinguish between competing microscopic mechanisms, providing novel insights into collective online behaviour.
