ASDK20007U Digital Methods

Volume 2022/2023

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.


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, 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.

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. online ethnography, content analysis, and exploratory network analysis.
  • Account for the procedures, potentials, and pitfalls of combining and integrating qualitative and quantitative data sources.
  • 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 network patterns, ideas, and behaviour characteristics of specific social settings and public issues, using the appropriate qualitative method(s).
  • Combine qualitative data with 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 specific method designs taught on the course.
  • Design and implement small-scale online ethnography campaigns, along with exploratory network analysis and content analysis, to obtain insights into social networks, ideas, and behaviour at individual and organizational levels.
  • 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.

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).

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.
  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 60
  • Exercises
  • 35
  • Project work
  • 63
  • Exam
  • 20
  • Total
  • 206
Continuous feedback during the course
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.
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 total length of the written assignment must not exceed:
• For three students: 20 standard pages
• For four students: 25 standard pages

In the standard situation (a 3-person group), the oral part of the exam lasts 40 minutes in total (30 minutes of examination). With extra student in group, 5 minutes are added.

ChatGPT and other large language model tools are permitted as a dedicated source, meaning text copied verbatim needs to be quoted, the tool cited, and generally the specific use made of them needs to be described in the submitted exam
Exam registration requirements

To be eligible for the exam in Digital Methods, it is a requirement that students have completed and passed four project-related assignments. The assignments can be submitted individually or in groups and must be approved by a member of the teaching team. The length of each assignment must be no longer than 3 standard pages.

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 assesment

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