Online Social Networks

Course Provider: Dr Bernie Hogan

Aims

The internet is but one of many networks. Every network is different in its own way but there are striking similarities, whether we refer to traffic routing, infectious diseases, friendships on Facebook or gossip on Twitter. This course represents a primer in social network analysis [SNA], a longstanding approach to the generation and analysis of network data.

SNA, also sometimes called structural analysis, has been at the forefront of many of the most considerable insights in sociology, from inequality in jobs, to political polarization. Yet, network analysis extends beyond sociology in important and significant ways. From computer science, we learn optimizations for graphs and new ways of visualizing them. From statistics, we learn which networks are likely to appear by chance. From physics, we learn of large scale cascading behaviour and ways of detecting communities. Collectively, these insights comprise a new field called network science.

In this course, we introduce many of the fundamentals of social network analysis, from graph theory through personal networks to newer network science approaches and advanced statistical modelling. Each week includes reading summaries and exercises designed to build the student’s capacity for network analysis. We conclude the course with a critical interrogation of network analysis to help circumscribe some limits to this otherwise exciting and powerful paradigm. The result is a well-rounded course designed to enable the effective use of networks in research.

This course is offered through the Oxford Internet Institute.

● 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?

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 taught in weeks 1-4 and 6-9 of Hilary term. The date, time and venue will be communicated to students during Michaelmas 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.

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. Your essay for this course can be in one of any of the following three styles:
1. A critical review of a concept in social network analysis. In this sort of essay, you will have to select a concept that has been introduced in the course and provide a review of the concept that includes a thorough review of empirical research, an outline of the outstanding issues with the concept and contemporary analytical or methodological approaches to the concept. This should also contain some novel synthesis rather than mere description.
2. A novel analysis of an existing data set. This is an analytical route that is suitable for a student planning to explore complex statistical approaches such as ERGms / SOAMs / big data analysis / network econometrics. As these models are tedious and slow to run as well as mathematically formidable, most of the work will involve the building and testing of the models alongside diagnostics and visualizations.
3. A descriptive analysis of novel data. This requires you to collect your own data. The emphasis in the essay will not be on complicated modeling so much as the methodological concerns involved in collecting the data. You will be expected to provide basic descriptives and visualizations where appropriate. You should report on the network(s) in such a way that their description will speak to a relevant research question.

Essays should be formatted using APA style and absolutely must contain a guiding research question. The essay topic should be agreed upon by the student and the course instructor prior to submission (see the following section).

Formative Assessment
Each week there will be a formative assignment. In weeks 1-4 the assignment will be a small analysis of a network done in an ipython notebook. In week 5 the student is expected to submit a final essay topic for approval. This will be a title and a <200 word summary of the topic. In weeks 6, 7 and 8 the students will again have short exercises to do in ipython (or related software) based on the course material. In week 9 the student will be expected to submit a preliminary piece of writing (between 1000-1500 words) that will form part of the final essay. The course instructor will provide written feedback of the writing and seek to schedule a meeting to discuss the writing after term ends and before papers are due. The purpose of this second piece is to demonstrate to the instructor that the proposed topic has sufficient literature / theoretical motivation and data to continue pursuing.

In addition to the formative assignments the course instructor will direct students to a wiki, housed at http://wiki.oii.ox.ac.uk/ . It will include headers for each of the readings for the weeks. Students are each expected to write a brief summary of at least two papers featured in the course. This way, by the end of course, every student will have a shared, thorough annotated bibliography to help them with their summative essay.

Submission of Assignments
All coursework should be submitted in person to the Examinations School by the stated deadline. All coursework should be put in an envelope and must be addressed to ‘The Chairman of Examiners for the MSc in Social Science of the Internet C/o The Clerk of Examination Schools, High Street. Students should also ensure they add the OII coversheet at the top of the coursework and that two copies of the coursework are submitted. Please note that all work must be single sided. An electronic copy will also need to be submitted to the OII. Please note that all coursework will be marked anonymously and therefore only your candidate number is required on the coversheet.

Please note that work submitted after the deadline will be processed in the standard manner and, in addition, the late submission will be reported to the Proctors' Office. If a student is concerned that they will not meet the deadline they must contact their college office or examinations school for advice. For further information on submission of assessments to the examinations school please refer to http://www.ox.ac.uk/students/academic/exams/submission/. For details on the regulations for late and non-submissions please refer to the Proctors website at https://www.admin.ox.ac.uk/proctors/examinations/candidates/.

Any student failing this assessment will need to follow the rules set out in the OII Examining Conventions regarding re-submitting failed work.

• Borgatti, Stephen P. Everett, Martin G. Johnson, Jeffrey C. Analyzing Social Networks. 2013. Thousand Oaks, CA: Sage.
• Hogan, B. (2017). Online Social Networks: Concepts for Data Collection. In N. Fielding, R. Lee, & G. Blank (Eds.), The SAGE Handbook of Online Research Methods (Second Ed, pp. 241–258). Thousand Oaks, CA: Sage.
• Crossley, N., Bellotti, E., Edwards, G., Everett, M. G., Koskinen, J., & Tranmer, M. (2015). Social Network Analysis for Ego-Nets. London, UK: Sage Publications. Ch. 3. Pp. 44-75
• Hogan, B., Melville, J., Phillips II, G., Janulis, P., Contractor, N., Mustanski, B., & Birkett, M. (2016). Evaluating the Paper-to-Screen Translation of Participant-Aided Sociograms with High-Risk Participants. In Proceedings of the 2016 Conference on Human Factors in Computing. CHI ’16. (pp. 5360–5371). San Jose, CA. http://doi.org/http://dx.doi.org/10.1145/2858036.2858368
• Hogan, B., & Wellman, B. (2014). The relational self-portrait: Selfies meet social networks. In M. Graham & W. H. Dutton (Eds.), Society and the Internet: How networks of information and communication are changing our lives (pp. 53–66). Oxford, UK: Oxford University Press.