Syllabus for Social Media and Digital Methods

Sociala medier och digitala metoder

Syllabus

  • 7.5 credits
  • Course code: 2IS060
  • Education cycle: First cycle
  • Main field(s) of study and in-depth level: Information Systems G1N

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (G), Pass with distinction (VG)
  • Established: 2017-05-18
  • Established by:
  • Revised: 2020-06-04
  • Revised by: The Department Board
  • Applies from: Autumn 2020
  • Entry requirements: General entry requirements
  • Responsible department: Department of Informatics and Media

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.

Content

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.

Instruction

Lectures, seminars, laboratory work, project work.

Assessment

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

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the University's disability coordinator or a decision by the department's working group for study matters.

Reading list

Reading list

Applies from: Spring 2022

Some titles may be available electronically through the University library.

  • Ignatow, Gabe; Mihalcea, Rada Text mining : a guidebook for the social sciences

    2017

    Find in the library

    Mandatory