Media and communication scholars and social scientists have always, at least implicitly, developed
theories about the structures and dynamics of networks. Today, the internet, online news, and social
media not only make these networks appear more visible, measurable, and complex, but their ubiquity
also creates a need to deal explicitly with the network paradigm – in theory, empirical research, and in
practice. In turn, networks, as models of interactions or relations, allow a researcher to tame phenomena
of organized complexity and therefore to analyse social phenomena from singular qualitative details,
through repeating patterns, up to global trends and comparisons in and between whole societies.
This class focuses on how questions that are relevant to the social sciences in general and communication
science in particular may be approached, using digital media data in combination with the visualization
and analysis of networks. The course will follow a hands-on approach, with short theoretical sessions
followed by coding and analysis challenges for which the participants will need to acquire new skills, using
a combination of Python and a network visualization tool (Gephi or Gephi Lite). They will be introduced to
behavioural trace data collection, network sampling, network modelling, (social) network measures,
community detection methods, network visualization as well as some basic (partly automated) content
analysis techniques to interpret the results. As part of a group project, participants will apply a set of
techniques that we have studied to a dataset or data collection of their choice.
