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You are here Postgraduate > MSc in Comparative Social Change > Course Structure and Handbook

Sociological Thinking in the Digital Age

Module Code: SOC41070 (UCD)

  • ECTS Credit : 10
  • Mandatory/ Optional : Optional
  • Module Coordinator : Dr Taha Yasseri, School of Sociology, UCD

Module Description:

The ongoing digital transformation of our societies has had quite a few “biproducts”, among them is the unprecedented amount of transactional digital data that we produce as we go about our daily lives, also known as big data. Big Data are being used to study the very same digital transformation that led to their generation, as well as more general and fundamental aspects of our social lives in the framework of computational social science and beyond. From the sociology point of view, a very first question to ask would be about the relevance of social theory to the “digitalized” study of humans. Some of the fundamental concept and theories in social studies were developed even before empirical sociology had become fashionable. Are those theories still relevant when machine learning is slowly becoming a common item in the toolset of social scientists? How is social theory being challenged, modified, and even ignored in our modern approach to studying humans and societies? How can social theory shape and motivate computational social science research? In this module, we seek to answer these questions through a short review of the main key concepts in sociology followed by an extended discussion on how they can be materialised and deployed in data-driven research through reviewing examples of successful and unsuccessful research programmes and analytical discussions.

Learning Outcomes:

On successful completion of the module students should be able to:

  • Understand the main sociological theories developed over the past two centuries to explain social phenomena;
  • Define and recognise Big Data and their differences; with the data generated in more traditional approaches such as surveys and interviews;
  • Understand the relevance of social theory to data-driven research;
  • Discuss the affordances and challenges in relation to materialising concepts central to sociological theory in the framework of data-driven research;
  • Outline the main modifications needed for a new framework of sociological theory that responds to a more solution-oriented sociology.

Lectures & Tutorials/ Contact hours:

  • Module Length: 12 weeks (Hilary Term)
  • Workload: Readings: 70hrs; Summative assessment (e.g. essays, journals): 130hrs. Total: 200 hours

Recommended Texts

Key Reading:

  • Barabási, A.L., & Albert, R. (1999) Emergence of scaling in random networks. science, 286(5439): 509-512.

  • Easley, D., & Kleinberg, J. (2010) Networks, crowds, and markets (Vol. 8). Cambridge: Cambridge University Press. Chapter 4, section 5 (pp. 107-116).

  • González-Bailón, S., Borge-Holthoefer, J., Rivero, A., & Moreno, Y. (2011) The dynamics of protest recruitment through an online network. Scientific reports, 1(1): 1-7.

  • Granovetter, M. S. (1973) The strength of weak ties. American journal of sociology, 78(6), 1360-1380.

  • Granovetter, M. (1978) Threshold models of collective behavior. American journal of sociology, 83(6): 1420-1443. Read the first 8 pages only (pp. 1428).

  • Manduca, R., & Sampson, R. J. (2019) Punishing and toxic neighborhood environments independently predict the intergenerational social mobility of black and white children. Proceedings of the national academy of sciences116(16): 7772-7777.

  • Merton, R.K. (1968) The Matthew effect in science: The reward and communication systems of science are considered. Science159(3810), 56-63.

  • Prendergast, C. (2005) Social capital. In G. Ritzer (Ed.), Encyclopedia of social theory (Vol. 1, pp. 716-717). Sage.

  • Rogers, E. M. (2010) Diffusion of innovations. Simon and Schuster.

  • Schelling, T.C. (1971) Dynamic models of segregation, Journal of Mathematical Sociology, 1(2): 143-186.

  • Tong, R. (2001) Feminist Theory. In Smelser, N.J. & Baltes, P.B. (Eds.) International encyclopedia of the social & behavioral science (Vol. 11). Amsterdam: Elsevier.

  • Wachs, J., Yasseri, T., Lengyel, B. & Kertész, J. (2019) Social capital predicts corruption risk in towns. Royal Society Open Science

  • Wagner, C., Garcia, D., Jadidi, M. & Strohmaier, M. (2015) It's a man's Wikipedia? Assessing gender inequality in an online encyclopedia. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 9, No. 1).

  • Watts, D. J. (2017) Should social science be more solution-oriented?. Nature Human Behaviour, 1(1): 1-5.


  • 3,000 word essay on a set of pre-defined topics (70%), Presentation (30%)