Visualizing Social Networks

In this project-based course, you will be given an opportunity to learn how to collect social network data from digital platforms and design social network graphs. Visualizations of social networks provide crucial insights into the cultural dynamics of online communities. The research skills which you can acquire during mini-research projects are invaluable to media studies and digital anthropology.

Digital platforms are an integral part of our everyday life, shaping the ways we experience social relationships, news, and entertainment. The back-ends of digital platforms constantly store data about the digital traces of their users. In this project-based course, you will be given an opportunity to learn how to collect social network data from digital platforms and design social network graphs. Visualizations of social networks provide crucial insights into the cultural dynamics of online communities. The research skills which you can acquire during mini-research projects are invaluable to media studies and digital anthropology. In the first sessions, we will discuss different theoretical approaches to studying social networks. You will furthermore learn techniques of retrieving social network data from digital platforms, including among other things Instagram, Facebook, Twitter, Snapchat, and TikTok. Finally, you will become familiar with the basic functionalities of the network visualization software Gephi and visione by designing your own network graphs.

While social network science is widely described as the study of the structural properties of networks, digital anthropology offers in-depth insights into digital cultures and sociality. Digital anthropologists provide in-depth studies of the appropriations of digital technologies within particular online communities, such as bloggers, software developers, hackers, and gamers (Miller 2018). The anthropological research process itself is increasingly complemented by the uses of digital technologies. Developing mini-research projects at the various intersections of social network analysis and digital anthropology, project participants will explore the digital traces of online communities, such as gamers, influencers, fans, and vloggers.

The project will include several practical sessions, in which you can explore an online community of your choice. By breaking into small groups at the beginning of the course, you will first become an expert on the online community you seek to research. In the second project phase, you will systematically gather relevant social network data for your topic and design social network graphs. Using the sigma.js library, you will also develop dynamic network visualizations that can be integrated into websites. The interim results of the mini-research projects will be discussed during the mid-term presentation day, allowing for peer-assessment and feedback. Online marketing managers and data scientists will be invited to an expert panel held in the final phase of the project. The experts will share their expertise and provide input to the students’ projects.

All the software applications which we will be using in this course are available as open-source packages. You are expected to download the suggested software on your own laptop at the beginning of the semester. The progress of the mini-research projects should also be documented by photographs or short screencasts for the final discussion.

The aim of the project

The overall aim of this course is to experiment with techniques of visualizing social networks and to understand how network visualizations can inform strategies for campaigns on digital platforms.

The main learning goals for project participants include:

  •  to increase data literacy
  • to distinguish between different theoretical perspectives of social network analysis
  • to become familiar with the basic vocabulary of social network analysis
  • to learn how social network data can be retrieved from the Application Programming Interfaces (APIs) of digital platforms
  • to learn the central procedures for “cleaning” social network data
  • to learn how to design insightful network graphs with cutting-edge network visualization software
  • to understand the different stages of managing a mini-research project

Interdisciplinary approach

This course will primarily provide an overview of the numerous theoretical perspectives on social networks, which will be complemented by a discussion of anthropological approaches to online communities. In the first two sessions of the project, the instructors will present various ways of researching social networks and online communities. In the course of the mini-research projects, social network graphs can be interpreted against the background of theoretical perspectives from both traditions. Project participants can acquire transferable, hands-on research skills throughout the course. They will also learn how to manage the scope of research projects, hold effective group meetings, and write concise research reports.

Research base and innovation

Grounded in recent scholarship in social network science and digital anthropology, this course provides various avenues for developing cutting-edge social network graphs and original analyses of contemporary online communities. Insights into the structural components of social networks can inform strategies for online campaigns on digital platforms. We will, for instance, identify key actors, connected communities, bridges, gatekeepers, weak ties, and liaisons in social networks. Such knowledge can directly inform the strategies of companies and non-governmental organizations. The interdisciplinary design of the course will enable a unique knowledge transfer between digital anthropology and computational social network analysis.

Project outcome

The most tangible outcomes of the project will be a roadmap, a website, and a repository on GitHub. The project is designed as a crowdsourcing experiment, which brings together the experiences of its participants, online marketing managers, and data scientists. The experiences from the mini-research projects will be continuously documented. The project team will first and foremost be working toward a roadmap outlining online strategies for content dissemination on digital platforms. This document will contain guidelines for companies and non-governmental organizations. The project team will furthermore develop a website to disseminate insights into the researched online communities and to showcase the network graphs generated within the mini-research groups. Finally, a repository will be created on GitHub, providing a discussion forum on social network analysis for digital humanities scholars.

Suggested Readings:

  • Abraham, A.; Hassanien, A. and Snasel, V. (eds.) (2010) Computational Social Network Analysis: Trends, Tools and Research Advances. Springer Press, London.
  • Boyd, D. and Kate Crawford, K (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society 15(5): 662-679.
  • Brath, R. and David, J. (2015) Graph analysis and visualization: Discovering Business Opportunity in Linked Data. John Wiley & Sons: Indianapolis.
  • Huberman, B.; Romero, D. and Wu, F. (2009) Social networks that matter: Twitter under the microscope. First Monday 14(1).
  • Miller, D. (2018) Digital Anthropology. In: F. Stein, S. Lazar, M. Candea, H. Diemberger, J. Robbins, A. Sanchez and Stasch, R. (eds.) The Cambridge Encyclopedia of Anthropology http://doi.org/10.29164/18digital [Accessed: 14/05/20]
  • Newman, M. (2018) Networks. Oxford University Press: Oxford.
  • Scott, J. and Carrington, P. (2011) The SAGE Handbook of Social Network Analysis.SAGE Publications Ltd: New York.
  • Scott, J. (2000) Social Network Analysis: A Handbook. SAGE Publications Ltd: New York.
  • Dataset for Practising: https://github.com/gephi/gephi/wiki/Datasets

Additional information

Ideally, the sessions of this course will be held in classrooms at Tallinn University. We are flexible to hold the sessions on Skype or Zoom, if required.

    Compulsory tasks

  • Creating LIFE project plan with the team members
  • Filling out LIFE mid-term report with the team members (by intermediate week)
  • Participating in the feedback session during intermediate week
  • Writing a self-reflective report about one’s role as a team member and about others’ responsibilities in project work (present individually to supervisor)
  • Put together LIFE project portfolio which should include the project report, action plan, media coverage and project evaluation
  • Participating as a team in the presentation day of LIFE projects and presenting the results of the activities carried out within the team
  • In order to complete the independent work, students are required to participate actively in the meetings arranged within the project and to fulfil the commitments made to the team

Supervisor(s)

YA

Yan Asadchy

cactus60@tlu.ee

CR

Christian Simon Ritter

christian.ritter@tlu.ee

Co-supervisor(s)

AM

Aet Möllits

aetm@tlu.ee

Project team

A

Anna

Kirjandus-, visuaalkultuuri ja filmiteooria

J

Jia

Kommunikatsioonijuhtimine

M

Mari-Ann

Organisatsioonikäitumine

D

Daniel

Informaatika

A

Anna

Kommunikatsioonijuhtimine

I

Iryna

Digitaalsed õpimängud

A

Andreas

Informaatika

R

Rene

Rakendusinformaatika

I

Isaac Odion

Rahvusvahelised suhted

Y

Yuhao

Kommunikatsioonijuhtimine

M

Marianne

Informaatika

J

Jamshid

Avatud ühiskonna tehnoloogiad

Y

Yaroslav

Kommunikatsioonijuhtimine

A

Ahmed Mohamed Said Anwar

Digitaalsed õpimängud

O

Olusanjo Michael

Poliitika ja valitsemine

A

Anastasia

Reklaam ja suhtekorraldus

O

Olga

Reklaam ja suhtekorraldus

O

Olamilekan Hammed

Poliitika ja valitsemine

I

Iryna

Digitaalsed õpimängud

S

Sander

Informaatika

R

Rohid

Avatud ühiskonna tehnoloogiad

A

Artem

Ristmeedia

D

Dinara

Sotsiaalne ettevõtlus

J

Juan Pablo

Kirjandus-, visuaalkultuuri ja filmiteooria

#social networks #digital platforms #data visualization