Social Network Analysis

Course title

Social Network Analysis.

Faculty

Dr. Bernie Hogan, Oxford Internet Institute, bernie.hogan@oii.ox.ac.uk.

Bernie Hogan (PhD, University of Toronto, 2009) is a Research Fellow at the Oxford Internet Institute, a multidisciplinary department at the University of Oxford. He has published widely on network data collection methods starting with pen and paper methods for personal networks, and moving through roster studies, Facebook networks, message boards and most recently data collection using touchscreen displays.

Course language

English.

Course schedule

June 20, 21 and 22, from 3:00pm to 8:00pm.

Description

Social network analysis is a powerful paradigm for understanding the relationship between individual level behaviors and larger social level outcomes.  Whether we are interested in trending memes, a sense of community among immigrants, international trade flows or the spread of communicable diseases, network analysis gives us a solid foundation for making more effective claims.

As a paradigm, social network analysis has been evolving considerably in the last half century. Since 2000 the array of data available to the researcher (on account of the increasing mediation of everyday life) has led to a profusion of potential forms of network analysis. Such advances have not only led to new analysis of online networks, but new techniques for capturing traditional personal networks. Further, the statistical tools for analyzing networks have becoming increasingly sophisticated; to the point where we can make causal claims about social selection and even analyze the entirety of the Facebook social graph.

This course is a graduate level introduction to social network analysis with a special focus on a variety of data collection methods for social networks. Simply because we can think of a network that 'could' be collected does not mean it is feasible. For both practical and ethical reasons, some networks are easier to obtain than others. We will guide the student through the various strategies for capturing networks as well as through the key approaches to the description and representation of these networks suitable for scientific publication. We will cover both online networks (primarily from Twitter and Facebook) as well as offline networks (either as a whole network study such as all members of an office or a personal network study where we compare networks or randomly sampled individuals). By the end of the course, the student will be able to complete a basic network analysis study from research question through data collection and basic descriptive and visual analysis. The skills from this course will equip students to collect data as well as seek out more specific analytical techniques for more sophisticated study.

Topics:

1. Social media networks from Twitter.

2. Visual data collection methods.

3. Descriptive analysis of networks.

4. Network typologies.

5. Sampling in networks.

Evaluation

Students will be expected to produce two outputs. The first (in a group setting) is to present a viable research design strategy for a social network study, based on a description of the population from the instructor. The second (as individuals) is to analyze a given network using the methods presented in class to illustrate key concepts.

Classroom

PC2