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Online Social Networks Online Social Networks Academic Year: 2014-15, Hilary Term Day and Time: Weeks 1-9, Day and time to be determined Location: TBC Course Providers: Dr Bernie Hogan, Oxford Internet Institute, [email protected] Dr Taha Yasseri, Oxford Internet Institute, [email protected] Background 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. Key Themes ● 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? Course Objectives 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 1 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. Learning Outcomes 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. Teaching Arrangements 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. Assessment 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 prior to submission (see the following section). Formative Assessment Each student will also be required to write one short essay (length: 1500-3000 words) stating the research question and methodological approach that will be developed in the final essay. Students are expected to submit a research question paragraph in Week 3 of Hilary Term, and the short essay in Week 7 of Hilary Term. Both are due on 5pm the day prior to class. This essay will provide a means for students to obtain feedback on the progress they have achieved prior to the final submission of their work. 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 department. 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. 2 For further information on submission of assessments to the examinations school please refer to http://www.admin.ox.ac.uk/schools/oxonly/submissions/index.shtml. For details on the regulations for late and non-submissions please refer to the Proctors website at http://www.admin.ox.ac.uk/proctors/info/pam/section9.shtml. Any student failing this assessment will need to follow the rules set out in the OII Examining Conventions regarding re-submitting failed work. Topics 1. Introduction and Research Design 2. Generating and representing networks 3. Basic network metrics I: Centrality and position 4. Basic network metrics II: Clustering 5. Modeling networks I: Triads and dependency models 6. Modeling networks II: Diffusion and generative models 7. Network visualization techniques 8. Theorizing networks General Readings The general readings are structured in essential and optional. Those items marked with an asterisk (*) are essential reading and MUST be read by all students in preparation for the class. If a reading is essential, it is because we use multiple chapters from the book and consider it a key reference for this course. The optional readings are resources that we return to on a regular basis, but will not help directly shape the course. We expect students to have purchased a (paper or digital) copy of the essential readings. * Borgatti, Stephen P. Analyzing Social Networks. 2013. Thousand Oaks, CA: Sage. Everett, Martin G. [ASN] Johnson, Jeffrey C. Henning, M. et al. Studying Social Networks. 2013. Berlin: Springer-Verlag. [SSN] Hansen, Derek Analyzing Social Networks with NodeXL. New York: Morgan Shneiderman, Ben Kaufman. [ANXL] Smith, Marc A. Newman, Mark Networks: An Introduction. 2010. Oxord, UK: Oxford University Press. [NAI] Week 1. Introduction and research design. Substantive: In this week we present an overview of the network-as-object and give a brief tour of key network science research. We discuss current journals, books and conferences currently relevant for advances in network science. We show how the network-as-object is based on some very simple mathematical assumptions but nevertheless appears to have a great degree of flexibility and analytical power. 3 Practical: We introduce the three tiers of network analysis software: end-user application, analysis packages and coding environments. We provide a walk-through for students to open and describe a network using all three. * Borgatti, Stephen et al. Analyzing Social Networks. 2013. Thousand Oaks, CA: Sage. [ASN] • Chapter 1: Introduction, pp. 1-10 * Hennig, Marina et al. Studying Social Networks. 2013. Berlin: Springer-Verlag. [SSN] • Chapter 1: Introduction, pp. 13-26 Hansen, Derek et al. Analyzing Social Networks with NodeXL. New York: Morgan Kaufman. • Chapter 3: Social Network Analysis: Measuring, Mapping, and Modeling Collections of Connections, pp. 31-51 Wellman, Barry “Structural analysis: From method and metaphor to theory and substance”. In Wellman, B., S. D. Berkowitz. (Eds). Social Structures: A Network Approach. 1988. Cambridge University Press, Cambridge, UK. pp. 19–61 Marsden, Peter V. “Recent Developments in Network Measurement”. In Carrington, P., J. Scott, S. Wasserman. 2006. Models and Methods in Social Network Analysis. Cambridge, UK: Cambridge University Press. pp. 8-30. Hogan, Bernie “The conceptual foundations of Social Network Sites and the emergence Wellman, Barry of the relational self-portrait”. In Dutton, W., M. Graham. Forthcoming. Society and the Internet. Oxford, UK: University of Oxford Press. Week 2. Generating and representing networks Substantive: Networks are representations of relationships between entities. In some cases these relationships are well defined, whereas in others they are inferred, assumed, or surveyed. We first describe the multiple ways data can be represented, as adjacency matrices, lists, sociograms and data structures.
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