Digital Methods

Course content

Using digital methods is a specific approach to doing digital social research. In digital methods, the focus is placed on the digital media contexts where data is generated as a by-product of social interaction, and on new ways of combining quantitative and qualitative methods of digital inquiry and analysis. This course provides students with practical skills in implementing three sets of computer-assisted qualitative methods: exploratory network analysis, digital online ethnography, and content analysis. As such, it supplements the various quantitative techniques taught in other courses on the degree programme, and provides tools for mixing qualitative methods with textual and/or visual quantitative data into qualitative-quantitative social-science analyses. Students train these skills by conducting their own integrated mapping of a public issue, involving networks, ideas, and behaviour across individual and organizational levels and across multiple digital platforms.


Mandatory course on MSc programme in Social Data Science at University of Copenhagen. The course is only open for students enrolled in the MSc programme in Social Data Science.

Learning outcome

At the end of the course, students are able to:


  • Show familiarity with the basic techniques, use scenarios, and validity criteria of computer-assisted qualitative methods, i.e. digital online ethnography, content analysis, and exploratory network analysis.
  • Account for the procedures, potentials, and pitfalls of combining and integrating qualitative and quantitative data sources., including in integrated qualitative-quantitative ways.
  • Account for the relationship between digital methods’ emphasis on the media contexts of digital data and the broader questions, claims, and biases of social data science.



  • Identify the procedures of qualitative content analysis for designing appropriate semantic categories, including for use in subsequent machine learning with quantitative text (and/or visual) data.
  • Extract, and communicate patterns of network patternss, ideas, and behaviour characteristics of specific social settings and public issues, using the appropriate qualitative method(s).
  • Combine qualitative data with a quantitative data sources, thereby integrating heterogeneous digital data formats into comprehensive social analyses.



  • Evaluate and analyse a social data problem from both qualitative and quantitative perspectives, including determining when to deploy which specific method designs taught on the course.
  • Design and implement small-scale online digital ethnography campaigns, along with exploratory network analysis and content analysis, to obtain insights into social networks, ideas, and behaviour at individual and organizational levels.
  • Make Produce persuasive qualitative-quantitative reports on social data problems for a range of organizational use scenarios, by integrating qualitative and quantitative sources of data as well as forms of narration and visualization.

Teaching combines lectures and in-class method exercises with extensive out-of-class project work. Throughout the course, students train their qualitative digital method skills by conducting their own project (with some teacher assistance available), i.e. digitally mapping a public issue chosen by themselves (or possibly suggested by the teachers). In-class exercises give priority to providing students first-hand skills in closely combining digital data formats into combined qualitative and quantitative social analyses that mirror realistic use scenarios in a range of contexts where social data analysis is a key component.

The syllabus consists mainly in relevant research articles pertaining to digital methods and the method skills and traditions covered. In addition, Robert V. Kozinets’ Netnography (Sage, 2019) is used as reader.

The syllabus altogether amounts to 600 pages. Of these, student groups self-select 40 pages of relevance to their chosen project theme (corresponding, standardly to 2 research articles).

Students must follow the Digital Methods course concurrently with Advanced Social Data Science II.

Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)
7,5 ECTS
Type of assessment
Written assignment
Oral examination, 40 mins. under invigilation
The exams for Digital Methods and Advanced Social Data Science II are combined into a single exam (see also section 6.6.), with separate assessment and grading.
Group-based oral exam with a prior written assignment in groups of 3-4 students. The written assignment should contain a description of method accounts, forms of analyses, and formulate and evaluate an explicit research question. In addition, it may contain documentation in the shape of code, field notes, data visualizations, and so on, as relevant to the project.
Specifics about the Digital Methods assessment:
The Digital Methods assessment is based on an overall assessment of the students’ ability to formulate and implement a coherent digital social research framework. Specifically, students are evaluated on their ability to give an integrated account of the different parts of the assignment (research question, methods, analysis, ASDSII section, etc.). Note that the content of the ASDSII section are not part of the Digital Methods assessment.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Criteria for exam assessment

The exam will be assessed on the basis of the learning outcome (knowledge, skills and competencies) for the course.

The written assignment and the oral exam are given equal weight towards the final grade awarded.

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 60
  • Exercises
  • 35
  • Project work
  • 63
  • Exam
  • 20
  • English
  • 206


Course number
7,5 ECTS
Programme level
Full Degree Master

1 block

Spring And Block 2
70 students.
Social Data Science
Contracting department
  • Social Data Science
Contracting faculty
  • Faculty of Social Sciences
Course Coordinator
  • Anders Blok   (3-64656f43767266316e7831676e)
Saved on the 15-12-2021

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