MG486 Half Unit
Social Computing, Data Analytics, and Information Services
This information is for the 2020/21 session.
Dr Antonio Cordella
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, MRes/PhD in Management (Information Systems and Innovation), 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.
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 smart phones, 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 organizations 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 most relevant data analytics technique used by social media and digital platforms to deploy personalization strategies as a means of boosting user platform engagement and generating data. It covers the current and emerging approaches in data extraction and analysis, personalization and communication, and digital experimentation, which shape the future of digital business strategy that build on big data and analytical thinking.
Overall, the course takes a unique approach to social media by examining the data-work they perform -data analytics techniques- from both the managerial and technical perspective.
The course blends theories, ongoing research insights, data analytics 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
• Understand techniques and methods of data extraction and analysis
• Understand personalization strategies and their implications
• Understand the purpose and principle of digital experimentation
• Design digital business strategy using big data and algorithmic thinking
• Acquire critical awareness of the current 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.
Written formative feedback is provided on the 500 words proposal for the summative essay.
1. Agarwal, R., & Dhar, V. (s2014). Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research. Information Systems Research, 25(3), 443–448.
2. Alaimo C. and Kallinikos J., (2017). Computing the everyday, The Information Society 33/4: 175-191.
3. 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.
4. 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.
5. Brynjolfsson, E. and McAffee, A. (2014). The second machine age. New York: Norton.
6. Brynjolfsson, E., & McElheran, K. (2016). The rapid adoption of data-driven decision-making. American Economic Review, 106(5), 133–39.
7. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4).
8. Helmond, A. (2015). The Platformization of the Web: Making Web Data Platform
9. Jacobides, M. et al. (2018) Towards a Theory of Ecosystems, Strategic Management Journal, 39/8, pp.2255-2276
10. Kitchin, R (2014). The data revolution: Big data, open data, data infrastructures and their consequences. London: Sage.
11. Konstan, J and Riedl, J. (2012) Recommended for you. Spectrum, IEEE, 49(10), 54-61.
12. McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68.
13. Parker, G, G, Van Alstyne, M. and Choudary, S. P. (2016). Platform revolution. London: Norton.
14. 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.
15. van Dijck, J. (2013). The culture of connectivity: A critical history of social media. Oxford: Oxford University Press.
16. Varian, H.R. (2010). Computer Mediated Transactions, American Economic Review 100(2): 1–10.
17. Varian, H.R. (2014). Beyond Big Data, Business Economics 49(1): 27–31.
18. Yoo, Y. et al. (2010), Research Commentary: The New Organizing Logic of Digital Innovation, Information Systems Research, 21/4: 725-735.
19. Zittrain, J. (2008) The future of the internet. New Haven: Yale University Press.
20. 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.
Important information in response to COVID-19
Please note that during 2020/21 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 situation of students in attendance on campus and those studying online during the early part of the academic year. For assessment, this may involve changes to mode of 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.
Total students 2019/20: 60
Average class size 2019/20: 15
Controlled access 2019/20: Yes
Value: Half Unit
Personal development skills
- Problem solving
- Application of information skills
- Commercial awareness