Typically, for analyzing and modeling social phenomena, networks are a convenient framework that allows for the representation of the interconnectivity of individuals and describing societal processes such as diffusion of information, epidemics, and spread of influence. In this workshop, we will discuss an alternative lens, that of data streams to which people are exposed. This perspective would allow us to use stream data mining tools to understand how people deal with massive and possibly infinite numbers of incoming data records, rapid arrival of data reaching the system, and modifications in the distribution of data in time.

During the workshop, we will ask the following questions:
1) How can we map relevant cognitive processes such as social learning to the ones that occur in interaction streams?
2) How useful are tools developed in machine learning to understand data streams in people’s social environments?