  Analysis of temporal network data arising from online interactive social
experiments is not possible with standard statistical methods because the
assumptions of these models, such as independence of observations, are not
satisfied. In this paper, we outline a modelling methodology for such
experiments where, as an example, we analyse data collected using the Virtual
Interaction Application (VIAPPL) --- a software platform for conducting
experiments that reveal how social norms and identities emerge through social
interaction. We apply our model to show that ingroup favouritism and
reciprocity are present in the experiments, and to quantify the strengthening
of these behaviours over time. Our method enables us to identify participants
whose behaviour is markedly different from the norm. We use the method to
provide a visualisation of the data that highlights the level of ingroup
favouritism, the strong reciprocal relationships, and the different behaviour
of participants in the game. While our methodology was developed with VIAPPL in
mind, its usage extends to any type of social interaction data.
