Getting started with social network analysis

I teach an MA course in Advanced Media Methodologies at the University of Cape Town. This  year I’m presenting an elective which introduces Media students to Social Network Analysis. I’m really looking forward to teaching the course and seeing how a conceptual grounding in social network analysis and the  techniques of visualisation will change the work my students are able to produce for their dissertations.

We don’t have much class time and there are so many new skills to be learned.  I decided to design the course around a series of exercises and readings that students can use to prepare before class.

Here is a first draft of the outline with the course readings and exercises. Any feedback welcome!

Analysing Social Media: Text, image, network


Early adopters (joined pre Dec 2007) in my own Twitter network
Early adopters (joined pre Dec 2007) in my own Twitter network

Week 1: Reading and exercise

Garton, L., Haythornthwaite, C., & Wellman, B. (2006). Studying Online Social Networks. Journal of Computer-Mediated Communication, 3(1).

  1. Create a blog (if you don’t have one already). You can use a free site such as You’ll be posting your answers to the class assignments on the blog.
  2. After reading the Garton et al (2006) reading for this week, prepare and pilot a short interview. Your interview should explore a research participant’s use of social media to communicate with his/her strong ties and should be designed to yield both quantitative and qualitative data. Post a short rationale for the interview questions on your blog and bring the questions to class next week.
Spreadsheet listing connections in our class
Spreadsheet listing connections in our class
  • Complete the Connections spreadsheet We will use this to map social networks during class.
    1. Click through to the editable spreadsheet on Google Drive
    2. Add your details to the final line of the spreadsheet.
    3. I have already added my details and the fact that I know all of you.
    4. Add your details by putting your name below the final line of data in the first column. In the second column, (next to your name), add the name of any other student you already know in the class, one per line. (I have already added the connections between the Interactive Media production students.
    5. In the third column, indicate from which class you already know that student.
    6. If you know the student from more than one class, add another line with your name, the student’s name and the name of the additional class.

Week 2: Readings and exercises

Hansen, D., Shneiderman, B., & Smith, M. A. (2010). Analyzing Social Media Networks with NodeXL.  Morgan Kaufmann. (Chapter 3) Chapter 10)

Bruns, A., & Burgess, J. (2012). Researching News Discussion on Twitter. Journalism Studies, 13(5-6), 801–814.

1. As shown to you in class, and using the vertex data from the Connections spreadsheet:

  • Download NodeXL and follow the installation instructions. You will need a Windows PC with Excel (or Windows and Excel installed on your Mac). You will also need internet access on the machine. NodeXL will not work on the UCT network behind the firewall.
  • Work through the NodeXL tutorial
  • Create a NodeXL sociogram to depict the relationships recorded in the Connections spreadsheet
  • Calculate the graph metrics. What are the various centrality measures? What do these numbers mean? What does this suggest to you?
  • Are there any clusters? What do you notice about them? What does this mean?
  • What is the graph density? What does this tell us?
  • How can you make the graph more readable?
  • Create a matrix to depict the relationships..
  •  How would you go about showing how everyone in the class communicates with fellow students and tutors about the social media assignments?
  • Do you have any criticism of the data we collected or how NodeXL represents it? How could we improve the data in the graph?

2.      Advanced (for students who want to use social network data for creative projects)

Week 3: Readings and exercises

Hansen, D., Shneiderman, B., & Smith, M. A. (2010). Analyzing Social Media Networks with NodeXL.  Morgan Kaufmann. (Chapter 10)

  • Papacharissi, Z., de Fatima Oliveira, M.: Affective News and Networked Publics: The Rhythms of News Storytelling on #Egypt. Journal of Communication. 62, 2, 266–282 (2012).
  • Lewis, S.C. et al.: Content Analysis in an Era of Big Data: A Hybrid Approach to Computational and Manual Methods. Journal of Broadcasting & Electronic Media. 57, 1, 34–52 (2013).
  1. Read Hansen et al. Chapter 10 and download your own set of Twitter data to explore and graph your own personal network on Twitter.
  2. Download Twitter search data for a keyword that interests you.

Optional (for creative projects):

  1. Read Chapters 6-11 Stanton, J. (2013). Introduction to Data Science.
  2. Conduct your own popularity contest to compare and graph Twitter activity around two words or phrases which are in the news right now.

Affording images: Digital imaging and media-sharing practices in a corpus of young people’s cameraphone images

Paper presented at Multimodality in Education colloquium held at Mont Fleur, Stellenbosch on 10 August, 2011 by Marion Walton and Silke Hassreiter, Centre for Film and Media Studies. University of Cape Town

The affordances of mobile phones as devices for creating, publishing and distributing images means that they are often seen as a threat to young people’s safety or to public morality. Alternatively, they are celebrated as having immense potential for supporting an individualised and highly networked mode of mobile learning or ‘m-learning’. These issues are particularly significant in the global South, where photographic practices and digital imaging are being adopted rapidly, as mobile networks reach over a billion people and feature phones with cameras become increasingly accessible.

This paper documents the image-sharing and photographic practices of fourteen young people who participated in a mobile video-making project over four months in July-November 2010 in Makhaza, Khayelitsha. We analyse the corpus of images which they shared with us as researchers. We explore distinct communicative genres which, in this context, are associated with (i) personal photographs, (ii) photographic composites (iii) downloaded images from popular culture (iv) multimodal image messaging. In this paper, our focus is specifically on interpersonal meanings and the representation of interpersonal meanings and social distance.

We argue that the social practices of young people and the marginal contexts of this appropriation play key roles in their domestication of mobile photography. Consequently, it is a mistake to assume that new genres and practices can simply be ‘read off’ by listing the features or affordances of the new generations of smart phones. Instead, it is necessary to consider a wider range of contexts and uses before the ‘affordances’ of the new medium can start to be understood. In particular, the differences associated with the specific contextual meanings of artefacts such as mobile phones, local genres of communication and interaction, and broader issues of access to communication infrastructure and mobility need to be considered. We argue that a contextualised study such as this should be conducted before embarking on the development of new curricula for learning or self-expression for young people.