MG486      Half Unit
Social Computing, Data Analytics, and Information Services

This information is for the 2021/22 session.

Teacher responsible

Prof Jannis Kallinikos and Dr Zhi Cheng

Dr Cheng is known as 'Aaron'.


This course is available on the CEMS Exchange, Global MSc in Management, Global MSc in Management (CEMS MiM), Global MSc in Management (MBA Exchange), MBA Exchange, MSc in Management (1 Year Programme), MSc in Management of Information Systems and Digital Innovation and MSc in Media and Communications (Data and Society). This course is available with permission as an outside option to students on other programmes where regulations permit.

Course content

The course is about the growing importance ordinary users assume in spinning the fabric of the Web and supporting the operations of social media platforms and networks. This social transformation of the Web that is often referred to as social computing is closely associated with the diffusion of potent lightweight technologies such as smartphones, tablet computers and wearables and the continuing development of advanced interactive software applications. It is also linked to architectural and other software-based innovations that help construct interoperable information systems and infrastructures. Taken together, these trends set the stage for the transition from a transaction-based Web (e.g. buying items) to a Web in which online interaction, talk and communication become the backbone activities for the production of data that are variously used by social media platforms to generate economic value.

In this context, social media platforms emerge as key entities that mark the social transformation of the Web and the production of services that accommodate a great deal of stakeholders, such as platform owners, platform users and third parties such as advertisers and digital analytics companies. The course deals with the ways by which social media platforms operate as business organisations by analysing how they engineer user participation to produce a computable data footprint that is subsequently used to develop a range of data-based resources and services. The course also deals with the state-of-the-art data analytics techniques and methods used by social media and digital platforms to deploy personalisation strategies as a means of boosting user platform engagement and generating data. It covers the current and emerging approaches in data extraction, aggregation, predictive computing, personalisation and recommender systems, which shape the future of digital business strategy that builds on big data, machine intelligence and analytical thinking.

Overall, the course takes a unique approach to social media by examining the data-work they perform — encoding, aggregating, and computing — from both the organisational, managerial and technical perspectives.

The course blends theories, ongoing research insights, data analytics concepts, techniques and real-life examples to analyse the social and economic implications of these significant developments.

• Explain the drives behind social computing

• Describe the technological developments and the architectural principles that govern social computing and the growing involvement of lay publics in the Web

• Link data-based practices with social systems and the digital economy

• Explain how social media platforms operate as business organisations

• Understand the formation of ecosystems and the role they play in sustaining the operations of social media platforms and the digital economy

• Describe social media as important actors in the digital economy

• Develop data-analytical thinking for management and business

• Understand techniques and methods of data extraction, database management and predictive analytics

• Understand data-driven information services (e.g. personalisation, recommender systems) and their social-economic implications

• Design digital business strategy using big data and algorithmic thinking

• Acquire critical awareness of the current and emerging digital economy and the ways it operates


20 hours of lectures, 10 hours of seminars and 3 hours of workshops in the LT.

There is a Reading Week in Week 6. There will be no teaching during this week.

Formative coursework

Written formative feedback is provided on the 500 words proposal for the summative essay.

Indicative reading

1. Alaimo, C. and Kallinikos, J. (2016). “Encoding the everyday: Social data and its media apparatus”, in Big data is not a monolith: Policies, practices, and problems, Sugimoto, C, Ekbia, H. and Mattioli M. (eds.) Cambridge, MA: The MIT Press, pp. 77-90.

2. Alaimo C. and Kallinikos J., (2017). Computing the everyday, The Information Society 33/4: 175-191.

3. Alaimo, C. & Kallinikos, J. (2019). Recommender Systems, in Beyes, T., Holt, R. and Pias, C. (eds.), The Oxford Handbook of Media, Technology and Organization Studies. Oxford University Press, pp. 401-411.

4. Alaimo, C. Kallinikos, J., & Aaltonen, A. (2020). Data and value. In Handbook of Digital Innovation. Edward Elgar Publishing, pp. 162-178.

5. Aaltonen, A., Alaimo, C. & Kallinikos, J. (2021). The Making of Data Commodity: Data Analytics as An Embedded Process. Journal of Management Information Systems (Forthcoming).

6. Aral, S. (2020). The Hype Machine: How Social Media Disrupts Our Elections, Our Economy, and Our Health-and how We Must Adapt. Currency.

7. Arthur, B. (2011). The Second Economy, McKinsey Quarterly, October 2011.

8. Baesens, B., Bapna, R., Marsden, J. R., Vanthienen, J., & Zhao, J. L. (2016). Transformational Issues of Big Data and Analytics in Networked Business. MIS Quarterly, 40(4), 807–818.

9. Baldwin, C. Y. & Woodard, C. J. (2009). The Architecture of Platforms: A Unified View. In A. Gawer, (ed.) Platforms, Markets and Innovation. Edward Elgar, Cheltenham, pp. 19–44.

10. Burkov, A. (2019). Machine Learning Engineering. LeanPub.

11. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4).

12. Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64–73.

13. Helmond, A. (2015). The Platformization of the Web: Making Web Data Platform

14. Hosanagar, K. (2019). A Human’s Guide to Machine Intelligence: How Algorithms Are Shaping Our Lives and How We Can Stay in Control. Viking.

15. Jacobides, M., Cennamo, C. and Gawer, A. (2018) Towards a Theory of Ecosystems, Strategic Management Journal, 39/8, pp.2255-2276

16. Kitchin, R (2014). The data revolution: Big data, open data, data infrastructures and their consequences. London: Sage.

17. Konstan, J and Riedl, J. (2012) Recommended for you. Spectrum, IEEE, 49(10), 54-61.

18. Lemahieu, W., vanden Broucke, S., & Baesens, B. (2018). Principles of Database Management: The Practical Guide to Storing, Managing and Analyzing Big and Small Data. Cambridge University Press.

19. Parker, G, G, Van Alstyne, M. and Choudary, S. P. (2016). Platform Revolution. London: Norton.

20. Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. O’Reilly Media, Inc.

21. Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS Quarterly, 553–572.

22. Van Dijck, J. (2013). The culture of connectivity: A critical history of social media. Oxford: Oxford University Press.

23. Yoo, Y. et al. (2010), Research Commentary: The New Organizing Logic of Digital Innovation, Information Systems Research, 21/4: 725-735.

24. Zuboff, S. (2015). Big other: surveillance capitalism and the prospects of an information civilization. Journal of Information Technology, 30(1), 75-89.


Essay (100%, 3000 words) in the LT.

Course selection videos

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.

Important information in response to COVID-19

Please note that during 2021/22 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the differing needs of students in attendance on campus and those who might be studying online. For example, this may involve changes to the mode of teaching delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.

Key facts

Department: Management

Total students 2020/21: 83

Average class size 2020/21: 18

Controlled access 2020/21: Yes

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

  • Problem solving
  • Application of information skills
  • Communication
  • Commercial awareness