English   Danish

2017/2018  KAN-CEBUV2031U  Big Social Data Analytics (T)

English Title
Big Social Data Analytics (T)

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for BSc/MSc in Business Administration and Information Systems, MSc
Course coordinator
  • Course Coordinator
    Ravi Vatrapu - DIGI
Administrative contact person is Jeanette Hansen at ITM (jha.itm@cbs.dk).
Main academic disciplines
  • Managerial economics
  • Information technology
  • Statistics and quantitative methods
Last updated on 13-02-2017

Relevant links

Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors: After completing the course, students should be able to
  • Characterize the phenomena of Big Social Data and Big Social Data Analytics
  • Analyze and apply different visual analytics concepts and tools for big social data sets
  • Apply set theoretical methods, techniques and tools for big social data analytics
  • Analyze and apply different concepts, methods, and tools for analyzing big social data in organizational contexts
  • Understand the linkages between business intelligence and business analytics and the potential benefits for organizations
  • Critically assess the ethical and legal issues in Big Social Data Analytics
Big Social Data Analytics:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 15 pages
Assignment type Project
Duration Written product to be submitted on specified date and time.
Grading scale 7-step scale
Examiner(s) One internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content and structure

This course is designed to provide knowledge of key concepts and methods of big social data analytics from a business perspective.  Course contents will cover issues in and aspects of manipulating, storing, and analysing big social data in order  to create organizational value. Topics will include:

Big Data & Big Social Data
Data Science and Computational Social Science

Set Theoretical Approach to Computational Social Science: Social Set Analysis

Data Mining & Machine Learning

Visual Analytics

Text Analytics
Predictive Analytics

Business Intelligence & Business Analytic
Applications to Private & Public Sectors

Datafication: Security, Governance, Regulation, Privacy & Ethics

Teaching methods
Tool Tutorials
Case Studies
Feedback during the teaching period
The teacher will give continous feedback during the course.
Student workload
Lectures 24 hours
Exercises 24 hours
Tool Workshops: Preparation and Participation 48 hours
Project Work 70 hours
Project Report 40 hours
Total 206 hours
Expected literature

Davenport, T. H., Barth, P., & Bean, R. (2012). How 'Big Data' Is Different. (cover story). MIT Sloan Management Review, 54(1), 43-46.

Hsinchun, C., Chiang, R. L., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188.

Noren, A. (ed.) (2011). Big Data Now. O’Reilly Media, Inc.


Vatrapu, R. (2013). Understanding Social Business. In K. B. Akhilesh (Ed.), Emerging Dimensions of Technology Management (pp. 147-158). New Delhi: Springer.


Robertson, S., Vatrapu, R., & Medina, R. (2010). Off the wall political discourse: Facebook use in the 2008 U.S. presidential election. Information Polity, 15(1,2), 11-31.


Hussain, A., Vatrapu, R., Hardt, D., & Jaffari, Z. (in press/2014).  Social Data Analytics Tool: A Demonstrative Case Study of Methodology and Software. In Gibson, R., et al (eds). Digital Methods, Palgrave Macmillan


Mukkamala, R., Hussain, A., & Vatrapu, R. (2014). Fuzzy-Set Based Sentiment Analysis of Big Social Data. Proceedings of IEEE EDOC 2014, Ulm, Germany.


Hussain, A., & Vatrapu, R. (2014). Social Data Analytics Tool: Social Data Analytics Tool: Design, Development and Demonstrative Case Studies.Proceedings of IEEE EDOC 2014, Ulm, Germany.


Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, P. K. (2010). Measuring User Influence in Twitter: The Million Follower Fallacy. ICWSM, 10, 10-17.


Asur, S., & Huberman, B. A. (2010, August). Predicting the future with social media. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on (Vol. 1, pp. 492-499). IEEE.


Lassen, N., Madsen, R., & Vatrapu, R. (2014). Predicting iPhone Sales from iPhone Tweets. Proceedings of IEEE EDOC 2014, Ulm, Germany.


Romero, D. M., Galuba, W., Asur, S., & Huberman, B. A. (2011). Influence and passivity in social media. In Machine learning and knowledge discovery in databases (pp. 18-33). Springer Berlin Heidelberg.


Chapter 27 of Munzner, T. (2009). Visualization. Fundamentals of Graphics, Third Edition. AK Peters, 675-707.



Heer, J., Bostock, M., & Ogievetsky, V. (2010). A tour through the visualization zoo. Commun. ACM, 53(6), 59-67.


Executive Summary Thomas, J. J., & Cook, K. A. (Eds.). (2005). Illuminating the path: The research and development agenda for visual analytics. IEEE Computer Society Press.


Fisher, D., DeLine, R., Czerwinski, M., & Drucker, S. (2012). Interactions with big data analytics. interactions, 19(3), 50-59.





Chris Zimmerman, Yuran Chen, Daniel Hardt, and Ravi Vatrapu. 2014. Marius, the giraffe: a comparative informatics case study of linguistic features of the social media discourse. In Proceedings of the 5th ACM international conference on Collaboration across boundaries: culture, distance & technology (CABS '14). ACM, New York, NY, USA, 131-140. DOI=10.1145/​2631488.2631501 http:/​/​doi.acm.org/​10.1145/​2631488.2631501


Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.


Danescu-Niculescu-Mizil, C., Kossinets, G., Kleinberg, J., & Lee, L. (2009). How opinions are received by online communities: a case study on amazon. com helpfulness votes. Proceedings of the 18th international conference on World wide web, 141-150.



Thomas, M., Pang, B., & Lee, L. (2006). Get out the vote: Determining support or opposition from Congressional floor-debate transcripts. Proceedings of the 2006 conference on empirical methods in natural language processing, 327-335.





Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational linguistics, 37(2), 267-307.


Constant, N., Davis, C., Potts, C., & Schwarz, F. (2009). The pragmatics of expressive content: Evidence from large corpora. Sprache und Datenverarbeitung, 33(1-2), 5-21.


Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188.


Chapters 01, 07, 10 & 11 of Saxena, R. & Srinivasan, A. 2013. Business Analytics: A Practitioner's Guide, Springer New York.


Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J. M., & Welton, C. (2009). MAD skills: new analysis practices for big data. Proceedings of the VLDB Endowment, 2(2), 1481-1492.


Davenport, T. H. (2006). Competing on analytics. harvard business review, 84(1), 98.

Last updated on 13-02-2017