Social Media and Digital Methods

7.5 credits

Syllabus, Bachelor's level, 2IS060

A revised version of the syllabus is available.
Education cycle
First cycle
Main field(s) of study and in-depth level
Information Systems G1N
Grading system
Fail (U), Pass (G), Pass with distinction (VG)
Finalised by
The Department Board, 18 May 2017
Responsible department
Department of Informatics and Media

Entry requirements

General entry requirements

Learning outcomes

Regarding knowledge and understanding, the student should be able to

  • explain essential concepts and technologies in the field of social media,
  • define, categorize and describe the most common forms of social media based upon the purposes they are used for,
  • explain how digital methods can be used for analysis of social media content and usage,
  • briefly describe digital methods for analysing data from social media, such as data mining, visualization, network analysis, content analysis and digital ethnography.

Regarding competence and skills, students should be able to

  • formulate a strategy for the analysis of social media including choice of methodology, data collection strategy and evaluation framework, in order to address a particular need,
  • conduct an analysis of social media content using basic digital methods,
  • interpret analyses of social media conducted with basic digital methods within the scope of the course.

Regarding critical evaluation and approach the student should be able to

  • discuss the implications of infrastructure, such as filtering algorithms, on analyses conducted with digital methods,
  • analyse and evaluate the use of social media and information extraction within information and communication work as well as journalism based upon ethical considerations.


Today´s Web 2.0, i.e., the user-generated web content such as blogs, microblogs and social media, creates enormous amounts of rich data related to societal development and public debate every day. These data are potentially interesting to many stakeholders, including researchers, journalists, social scientists, politicians and corporate communicators. However, these data are also extremely challenging to analyse, e.g. because of large volumes, unstructured and varying forms, and rapid generation.

This course will introduce students to basic digital methods for anlysis of social media data, such as data mining, visualization, network analysis, content analysis and digital ethnography. The course will introduce different forms of social media as well as their historcial development. The challenges and limitations relating to analysis of social media data will be discussed in depth, and different methods for preparing and analysing these data will be presented. Students will apply different basic digital methods to conduct analyses of, e.g., Twitter data. Finally, consequences of algorithmic aspects of social media, e.g., through the concept of filter bubble, will be discussed, as well as ethical aspects of analysis of social media data.


Lectures, seminars, laboratory work, project work.


Written exam, seminars, laboratory work, assignments, project work.

Other directives

The course does not give eligibility to apply for continuing courses in information systems.