One of the big social science questions is how our individual actions aggregate into collective patterns of behaviour (think crowds, riots, and revolutions). This question has so far been difficult to tackle due to a lack of appropriate data, and the complexity of the relationship between the individual and the collective. Digital trails are allowing Social Scientists to understand this relationship better. Small changes in individual actions can have large effects at the aggregate level; this opens up the potential for drawing incorrect conclusions about generative mechanisms when only aggregated patterns are analysed, as Schelling aimed to show in his classic example of racial segregation. Part of the reason why it has been so difficult to explore this connection between the individual and the collective—and the unintended consequences that arise from that connection—is lack of proper empirical data, particularly around the structure of interdependence that links individual actions. This relational information is what digital data is now providing; however, they present some new challenges to the social scientist, particularly those who are used to working with smaller, cross-sectional datasets. Suddenly, we can track and analyse the interactions of thousands (if not millions) of people with a time resolution that can go down to the second. The question is how to best aggregate that data and deal with the time dimension. Interactions take place in continuous time; however, most digital interactions are recorded as events (i.e. sending or receiving messages), and different network structures emerge when those events are aggregated according to different windows (i.e. days, weeks, months). We still don’t have systematic knowledge on how transforming continuous data into discrete observation windows affects the networks of interaction we analyse. Reconstructing interpersonal networks (particularly longitudinal network data) used to be extremely time consuming and difficult; now it is relatively easy to obtain that sort of network data, but modelling and analysing them is still a challenge. Another problem faced by social…
Small changes in individual actions can have large effects at the aggregate level; this opens up the potential for drawing incorrect conclusions about generative mechanisms when only aggregated patterns are analysed.