Online Social Networks
Availability: Optional for OII MSc and DPhil students.
Schedule: Hilary Term (Weeks 1-8). Wednesdays 11:00-13:00.
Location: Seminar Room, Oxford Internet Institute, 1 St Giles, Oxford OX1 3JS.
Reading list: Online Social Networks
The paradigm of network science is one that directly speaks to the web. With its hyperlink structure, relational tables, friend lists and constant stream of information diffusion, network analysis appears to be an obvious route to the analysis and understanding of the Internet’s dynamics. Such analyses are not mere passive reflections of data - the algorithms that power Google, Amazon, Facebook and Twitter are based in network science. Beyond the use of formal algorithms for network analysis are questions of societal import such as the consequence of the number and structure of Facebook friends; the overlap of personal network members on many media; the cascading behaviour of political activism; and the salience of identity in threaded conversations to name a few relevant topics.
In this course we introduce social network analysis and the more recent notion of ‘network science’ with particular emphasis on research design, data collection and analysis. We take a comparative approach to network topics, such as evaluating different measures of centrality, multiple approaches to clustering and variations on visualization. In doing so, it is our goal to not merely familiarize the student with the basics of network analysis capture and analysis, but to enable the student to make informed choices for analysis based on research questions rather than default tools or outmoded conventions.
What differentiates social networks as analytical objects from the reality they seek to represent?
How do the descriptive measures of networks inform us about macro social structures as well as micro social behaviours?
How do the affordances and constraints of online technologies help facilitate certain kinds of network structures (and indeed, even the notion of networks as analytical tools in the first instance)?
Why do networks as visual objects persist in having a rhetorical power? Is it that they are merely ‘sciency’ and complex looking or should we consider the visual presentation of networks as a meaningful scholarly practice?
‘The course will familiarise students with the state of network science as a paradigm comprising multidisciplinary approaches to the analysis of relational data. Students will be able to read introductory network metrics and understand how these measures speak to theories of human behaviour as well as put together an original piece of analysis using network data. Students will gain a modest understanding, via the ‘sociology of science’, as to why network analysis is a highly distributed field where no single software application, journal or conference covers all of the active research on social networks. Students will also learn basic data capture and analysis techniques that can enable them to begin, if not complete, a full social network analysis study.
Upon successful completion of this course students should:
Have a familiarity with the basic terms and concepts of social network analysis.
Understand how differing network analysis metrics relate both to each other and to academic research questions.
Be able to describe how a network can be constructed from an online phenomenon.
Have a clear understanding of some of the various analytical tools used in network science.
Be able to construct and theorise a research question that employs social network analysis in order to address a specific topic related to human behaviour and collective dynamics.
The course will consist of eight classes, one in each week of the Hillary term. Each class will begin with an hour-long lecture. The second half of the class is typically a guided walkthrough of network analysis techniques. The techniques draw upon a variety of software packages and data sources. Every effort will be made to ensure cross-platform and open source software is used whenever possible, but this cannot always be guaranteed.
Introduction and Research Design
Generating and representing networks
Basic network metrics I: Centrality and position
Basic network metrics II: Clustering
Modelling networks I: Triads and dependency models
Modelling networks II: Diffusion and generative models
Network visualization techniques
Students will be assessed through a final essay that is no longer than 5000 words which must be submitted to the Examinations School by 12 noon of Monday of Week 1 of Trinity Term. The essay should consolidate a review of current literature, a theoretically informed research question about online social networks and network-oriented methodology that was featured in the course. The essay topic should be agreed upon by the student and the course instructor(s) prior to submission.