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2024/2025  KAN-CDIBV2406U  Concepts in Social Data Analytics

English Title
Concepts in Social Data Analytics

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
Max. participants 90
Study board
Master of Science (MSc) in Business Administration and Digital Business
Course coordinator
  • Abid Hussain - Department of Digitalisation (DIGI)
Main academic disciplines
  • Customer behaviour
  • Information technology
  • Innovation
Teaching methods
  • Blended learning
Last updated on 06-02-2024

Relevant links
Learning objectives
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
Course prerequisites
This course cannot be taken together with the course CCMVV2556U Big Data Analytics due to overlap
Examination
Concepts in Social Data Analytics:
Exam ECTS 7,5
Examination form Written sit-in exam on CBS' computers
Individual or group exam Individual exam
Assignment type Written assignment
Duration 2 hours
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter
Aids Closed book: no aids
However, at all written sit-in exams the student has access to the basic IT application package (Microsoft Office365 (minus Excel), document camera and paper, 7-zip file manager, Adobe Reader DC, PDF24, Texlive, VLC player, Windows Media Player – ATTENTION no sound allowed), and the student is allowed to bring simple writing and drawing utensils (non-digital). PLEASE NOTE: Students are not allowed to communicate with others during the exam.
Make-up exam/re-exam
Same examination form as the ordinary exam
The number of registered candidates for the make-up examination/re-take examination may warrant that it most appropriately be held as an oral examination. The programme office will inform the students if the make-up examination/re-take examination instead is held as an oral examination including a second examiner or external examiner.
Course content, structure and pedagogical approach

This course is designed to provide knowledge of key concepts and methods of big social data analytics from a business perspective. 

 

The course will provide students with conceptual understandings as well a practical skills to handle big social data analytics. Building on set-theory and analytics, the course equips the student with the ability to generate valuable business insights for companies and organizations. Blending theoretical foundations with tool tutorials, exercises and demonstrations, the sessions on the course address the technical foundations for meaning making of social data. The course equips students with knowledge of end-to-end analysis processes enablins the students to run big data analytics processes in their own companies, in entrepreneurial contexts as well as for larger organizations.

 

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

 

 
Description of the teaching methods
Lectures
Exercises
Tool Tutorials
Demos
Case Studies
Feedback during the teaching period
There will be three ways to provide feedback. a) Two online quizzes with multiple choice questions and implicit feedback with survey results. b) Individual meetings for discussion about topics covered and exam project c) In person feedback during exercises.
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

The literature can be changed before the semester starts. Students are advised to find the final literature on CANVAS before they buy any material.

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.
 

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

  

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.

  
   

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.

    

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.

    

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.

    
Last updated on 06-02-2024