Digital Methods

Course content

Using digital methods is a specific approach to doing digital social research. In digital methods, 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 ethnography, and content analysis. As such, it supplements the various quantitative techniques taught in other courses on the program, as well as provides tools for mixing qualitative methods with textual and/or visual quantitative data into quali-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


  • Show familiarity with the basic techniques, use scenarios, and validity criteria of computer-assisted qualitative methods, i.e. digital ethnography, content analysis, and exploratory network analysis.
  • Account for the procedures, potentials, and pitfalls of combining qualitative and quantitative data sources, including in integrated quali-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 networks, ideas, and behaviour characteristic of specific social settings and public issues, using the appropriate qualitative method(s).
  • Combine qualitative data with a quantitative data source, 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 method designs.
  • Design and implement small-scale 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.
  • Combine qualitative and quantitative sources of data, as well as forms of narration and visualization, into persuasive quali-quantitative reports on social data problems for a range of organizational use scenarios.

Teaching combines lectures and in-class method exercises with extensive out-of-class project work. Throughout the course, students train their qualitative method skills by conducting their own project, i.e. digitally mapping a public issue (with some teacher assistance available) chosen from within a unifying theme (e.g. activism, sustainable transition, or similar). In-class exercises gives priority to providing students first-hand skills in closely combining digital data formats into composite social analyses, both qualitative and quantitative, in ways 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 can only register for the exam for Digital Methods if they have passed all compulsory courses on the first semester on the master's programme in social data science.

Students must follow Digital Methods concurrently with the Advanced Social Data Science II course on the master's programme in social data science.

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
Type of assessment details
Group-based oral exam with a prior written assignment in groups. 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.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship

An essay, written either in groups, or individually, on a subject pertaining to the course content and prescribed literature. The subject must be pre-approved by the course lecturer(s).

Criteria for exam assessment

The 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 account of the different parts of the assignment (research question, analysis etc.)


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

  • 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

Block 4
70 students.
Social Data Science
Contracting department
  • Social Data Science
Contracting faculty
  • Faculty of Social Sciences
Course Coordinator
  • Anders Blok   (3-696a74487b776b36737d366c73)
Saved on the 01-05-2024

Are you BA- or KA-student?

Are you bachelor- or kandidat-student, then find the course in the course catalog for students:

Courseinformation of students