Introduction to Computational Social Systems (706.020)

Course page for winter semester 2023/24


Welcome to Introduction to Computational Social Systems! This course will cover an overview of approaches to Computational Social Systems from the perspectives of Computer Science, Sociology, Business Administration, Psychology, and Law studies. Students in this course become familiar with the interdisciplinary bridges between approaches and how current research is connecting disciplines. A successful student of this course will be able to discuss a topic in Computational Social Systems from the viewpoint of various disciplines. It takes the form of a “Ringvorlesung” with a weekly talk by an expert in aspects of Computational Social Systems from computational, legal, sociological and psychological perspectives.

Lecture schedule & key dates

  • Oct 5: CSS Festival: Introduction to the course by institutional leads Elisabeth Lex and Stefan Thalmann, with attendance of other lecturers
    [NOTE: Takes place on 5 October 2023, at TU-Graz Infeldgasse 13 (HS i9 EG), starting at 4.00 p.m]
  • Oct 9: Viktoria Pammer-Schindler
    Designing technologies as parts of socio-technical systems
  • <Oct 15: Selection of Group Projects>
  • Oct 16: Tony Ross-Hellauer
    Open, responsible and reproducible research: introduction to theory and methods
  • Oct 23: Guilherme Wood
    Understanding, shaping, and interacting with the human cognitive architecture
  • Oct 30: Matthias Wendland (Online)
    Law by Design: Legal frameworks for human-centered socio-technical systems 
  • Nov 6: Jurgen Fleiss
    The Business Perspective of Computational Social Systems
  • <Nov 13: Semester project feedback session>
  • Nov 20: Markus Hadler
    Digitizing Sociology: Established approaches and new digital sources
  • Nov 27: Elisabeth Lex
    Data-driven human behavior modeling: from digital trails to algorithms
  • Dec 4: Eduardo Veas
    Towards Humane Centered Computing
  • Dec 11: Fariba Karimi
    Network Inequality: Measuring inequalities in networks and network-based algorithms
    <Jan 07 Hand-in of group project reports>
  • Jan 08: Student presentations
  • Jan 15: Student presentations
  • Jan 22: Student presentations
  • Jan 29: Wrap-up

Where and when

Where: HS 47.01 (Regilind und Irmingard von Admont) (0047EG0058) (KFU campus)

When: 10.00 to 12.00

Course assessment

The course assessment is based on student group projects (max 4 students). Student groups must mix students of at least two different majors. Group name lists have to be communicated before Oct 25th to Student projects should be developed during the course and presented in one of the last three sessions (Jan 2024), with all students in the project participating in the presentation. Course lecturers can ask questions to the students after each presentation. By the end of the course, a short written report on the project (max 8 pages, single column, 11pt) has to be submitted. Two additional dates will be announced for written replacement examination dates, but we encourage students to participate in the student projects instead.

Information about student projects: Students develop a research topic for their projects, covering the necessary steps for a larger project that motivates an empirical analysis to be done in other courses (e.g. Foundations of Computational Social Systems). Lecturers will compose a list of project topics and suggested readings by October 9th, from which the students can select project topics. You can also select your own topic for your project as long as it is clearly related to Computational Social Systems.

Parts that should be included in a project:

  • Motivation and background
    • Topic motivation and relevance for research and society at large
    • Brief literature review on the topic
    • Student’s position on the topic: missing research questions for future studies and possible approaches to address them
  • Proposal for a larger project
    • Main research gap addressed and why, including clear statement of Research Question(s)
    • Methods (including interdisciplinary considerations and differences between approaches)
    • Time-plan
    • Statement of foreseen impact of research
  • Small pilot study
    • Case-study or smaller-scale version of main project proposal as proof-of-concept
    • Small-scale piloting to investigate/test feasibility of aspect of your approach, methods, data-sources, etc.

Grading – weight and dimensions: Semester projects will be assessed according to the following criteria:

  • Semester project plan (25%) 
    • 1 Page motivation
    • 1 page RQ and goals
    • 1 page literature 
    • 1 time plan
  • Semester project report (50%) 
    • Motivation of the topic
    • Description of single papers
    • Independent literature research (not extensive, but needs to exist! and be reasonable)
    • Discuss and compare different perspectives
    • Overall coherent text
    • Correct citations according to scientific standards 
  • Semester project presentation (25%)
    • Quality of the presentation