English   Danish

2026/2027  KAN-CDIBV2603U  Concepts in Social Data Science

English Title
Concepts in Social Data Science

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
Study Board for Digitalisation, Technology and Communication
Programme Master of Science (MSc) in Business Administration and Digital Business
Course coordinator
  • Cecilie Steenbuch Traberg - Department of Digitalisation (DIGI)
Main academic disciplines
  • Information technology
  • Methodology and philosophy of science
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 20-01-2026

Relevant links
Learning objectives
After completing the course, students should be able to
  • Characterise key concepts, theories, and approaches in social data science and explain how social data can be used to analyse behaviour, interaction, and influence.
  • Utilise diverse forms of social data, including digital trace data, text data, experimental data, and simulated data, and evaluate their strengths and limitations.
  • Analyse how artificial intelligence systems shape social data, digital behaviour, and information flows, and evaluate their implications for research, organisations, and society.
  • Apply analysis methods for studying social processes such as social contagion, influence, trust, networks, and information flow, and discuss their implications for organisational and societal contexts.
  • Design and evaluate social data research approaches, including social experiments, social network analysis, and agent-based modelling, for investigating specific social phenomena.
  • Interpret the results of social data analyses and relate them to relevant theoretical perspectives and to applied challenges in organisational and societal settings.
  • Critically assess ethical, legal, and governance challenges in the collection, analysis, and use of social data.
  • Implement core social data science methods in R, including data preparation, modelling, visualisation, and basic statistical analysis.
Examination
Concepts in Social Data Science:
Exam ECTS 7,5
Examination form Written sit-in exam on CBS' computers
Individual or group exam Individual exam
Assignment type Written assignment
Duration 4 hours
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter
Aids Limited aids, see the list below:
The student is allowed to bring
  • An approved calculator. Only the models HP10bll+ or Texas BA ll Plus are allowed (both models are non-programmable, financial calculators).
  • Language dictionaries in paper format
The student will have access to
  • Advanced IT application package
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.
Description of the exam procedure

Students are provided with a dataset and a set of questions based on this material.

 

The questions include conceptual discussion, data analysis and interpretation tasks, as well as reflection on methodological choices, ethics, and the role of artificial intelligence in social data.

 

Students are expected to use R for exploratory analysis of the dataset as part of their answers.

 

To support the examination process, students are provided with a limited R reference sheet (cheatsheet) as well as guiding (but not fully specified) coding prompts.

 

Full technical implementation of all methods is not required; the assessment focuses on analytical reasoning, methodological judgement, interpretation of results, and discussion of their implications for organisational and societal contexts.
Course content, structure and pedagogical approach

This course introduces the core concepts, theories, and methodological approaches of social data science, with a focus on understanding digital behaviour in online environments and applying these insights to real organisational and societal challenges.

 

Students learn how to work with diverse forms of social data to investigate social processes such as influence, trust, emotion, networks, information flow, and AI-mediated interaction, and to understand how these processes shape behaviour in a range of real-world settings. The course also examines how artificial intelligence shapes the production, structure, and interpretation of social data, as well as the social environments in which this data is generated.

 

The course combines conceptual foundations from the social sciences with hands-on analysis skills, anchored in real-world problems and applications. Students learn how to collect, model, and analyse social data, and how to interpret results in theoretically meaningful and practically relevant ways.

 

Emphasis is placed on connecting methods to substantive social questions rather than treating tools in isolation. By the end of the course, students will be able to design and execute end-to-end social data analyses and translate insights into organisational and societal value.

 

Course contents address how to create, handle, analyse, and interpret social data for applied use. Topics will include:

 

• Foundations of Social Data Science
• Digital Behaviour, Influence, and Social Contagion
• Social Network Analysis
• Natural Language Processing and Language-Based Data
• Agent-Based Modelling of Social Systems
• Social Lab Experiments and Field Experiments
• Artificial Intelligence, Algorithmic Environments, and Information Flows
• Ethics and Governance of Social Data
Research-based teaching
CBS’ programmes and teaching are research-based. The following types of research-based knowledge and research-like activities are included in this course:
Research-based knowledge
  • Classic and basic theory
  • New theory
  • Teacher’s own research
  • Methodology
  • Models
Research-like activities
  • Development of research questions
  • Data collection
  • Analysis
  • Discussion, critical reflection, modelling
Description of the teaching methods
Lectures
Workshops
Exercises
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
Workshops/Exercises 24 hours
Preparation for Classes 158 hours
Exam and Preparation for exam 60 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.
 

Hackenburg, K., Tappin, B. M., Hewitt, L., Saunders, E., Black, S., Lin, H., Fist, C., Margetts, H., Rand, D. G., & Summerfield, C. (2025). The levers of political persuasion with conversational artificial intelligence. Science, 390, eaea3884.

 

Mosleh, M., Pennycook, G., & Rand, D. G. (2022). Field experiments on social media. Nature Human Behaviour, 6, 1473–1483.

Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. PNAS, 111(24), 8788–8790.

 

Traberg, C. S., Harjani, T., Roozenbeek, J., & van der Linden, S. (2024). The persuasive effects of social cues and source effects on misinformation susceptibility. Scientific Reports, 14, 4205.

 

Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2019). Social data: Biases, methodological pitfalls, and ethical boundaries. Frontiers in Big Data, 2, 13.

Rainie, L., & Wellman, B. (2019). The internet in daily life: The turn to networked individualism. In Society and the Internet (2nd ed.). Oxford University Press.

 

Arguedas, A. R., Robertson, C. T., Fletcher, R., & Nielsen, R. K. (2022). Echo chambers, filter bubbles, and polarisation: A literature review. Reuters Institute.

Ferdous, M., & Anwar, M. M. (2023). Identification of influential users in online social networks: A brief overview. Journal of Computer and Communications, 11(7), 58–73.

Gjurković, M., & Šnajder, J. (2018). Reddit: A gold mine for data-driven personality prediction. Workshop on Computational Modeling of Opinions.

 

Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data: A new framework for machine learning and the social sciences. Princeton University Press.
 

Centola, D., & Macy, M. (2007). Complex contagions and the weakness of long ties. American Journal of Sociology, 113(3), 702–734. 

 

Gilbert, N. (2019). Agent-Based Models (2nd ed.). SAGE Publications Ltd.

 

Wickham, H., & Grolemund, G. (2017). R for Data Science. O’Reilly Media. 


 

  
Last updated on 20-01-2026