Computational Social Sciences
Computational Social Sciences
DE
Guided
Computational Social Sciences

Computational Social Sciences

University of Vienna

Univ.-Prof. Mag. Dr. Sylvia Kritzinger

About
Duration 6 units
Unit 2 hours/unit
Licence CC BY 4.0
Participants 84
Availability Unlimited
Start Date 1 October 2022
Costs € 0.00

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General Course Information




Are you interested in methodological procedures and innovations in research and teaching in the social sciences and data science? Then you have come to the right place!

This MOOC is aimed at students of social science disciplines as well as students of Data Science; but also at all those who are interested in interdisciplinary research and teaching. The Computational Social Sciences are an interdisciplinary scientific approach to grasping, analysing, modelling and simulating social, i.e. social, political or communicative, phenomena. This analysis is done with methods from computer science, mathematics and statistics based on social science data, such as survey or text data from traditional and social media.

The idea for the Computational Social Science MOOC came from the cooperative digitisation project 'Digitize! Digitize is a project in which political science, communication science and sociology work together with data scientists, law and research ethics. The project deals with the various forms and dimensions of digitalisation as well as the social transformations that accompany it. Changes such as the increasing datafication of individual and social spheres of life present both the social sciences and the data sciences with new tasks and challenges.

Content

Course Content

In this course you will learn about digital data collection and data management and digital data analysis methods in computational social science research, relevant legal, ethical and social issues will be explained, you will get an insight into application examples of interdisciplinary research in computational social sciences. And! - current research topics as well as challenges for future Computational Social Sciences research are presented.

Among others, the following questions will be addressed:

  • What are the paradigmatic foundations and methodological approaches of the social sciences and the data sciences?
  • How do they differ and what do they have in common?
  • Which questions can be researched from the interdisciplinary perspective of Computational Social Science? 
  • What are concrete application possibilities for Computational Social Sciences?
  • What legal, ethical and social questions or rules must be observed for research in the Computational Social Sciences?
  • How does data management work in computational social science research?
  • Which questions can be researched from the interdisciplinary perspective of Computational Social Science?

Course Goals

After successfully completing all lessons, you will have acquired the following competences:

  • You can define the research area of Computational Social Sciences and name advantages of the interdisciplinary approach
  • You know different application possibilities for interdisciplinary research projects between the data sciences and the social sciences (computational social sciences) and can identify and evaluate such research questions.
  • You will be able to identify and evaluate the possibilities and limitations of basic methods of the data sciences and the social sciences.
  • You will be able to understand examples of possible applications in CSS research and evaluate them with regard to the advantages and disadvantages of collaboration/interdisciplinarity.
  • You can identify legal and ethical challenges of computational social science research and recognise the need for more in-depth information.
  • You know the basic steps of CSS data management and can name examples of (potential) optimisations through data science methods.
  • You know concrete examples of CSS research that are intended to go beyond the state of the art in the social sciences and data sciences.

Previous Knowledge

Basic knowledge of empirical research and English language skills are recommended for participation in the MOOC.

Course Procedure

Unit 1: Introduction: What is Computational Social Sciences?

    1.1: Why do we need computational social sciences?
    1.2: Computational Social Sciences from an interdisciplinary perspective

Unit 2: An Overview of the Foundations and Methodological Perspectives of the Social Sciences and the Data Sciences

    2.1: Social Sciences and Data Sciences: Approaches, Differences and Overlaps
    2.2: Fundamentals of the Data Sciences
    2.3: Methods of the Data Sciences
    2.4: Paradigms and foundations of the social sciences
    2.5: Methodological approaches in the social sciences

Unit 3: Digital Data Collection and Data Management in Computational Social Science Research

    3.1: CSS data collection procedures and methods and selected examples
    3.2: Tools for Computational Social Science Research: Application Programming Interface
    3.2: Data Management in Computational Social Sciences

Unit 4: Digital Data Analysis Techniques in Computational Social Science Research

    4.1: Data Analysis Techniques in the Computational Social Sciences: Between Data and Social Sciences
    4.2: Techniques and Methods of Data Analysis in Computational Social Science Research
    4.3: Data Visualisation in Computational Social Sciences

Unit 5: Legal and Ethical Aspects of Digital Computational Social Science Research

    5.1: Legal foundations and research ethics in computational social sciences: an overview
    5.2: Law and ethics in the context of digital research (using Digitize! as an example)

Unit 6: Digitize! - Computational Social Sciences in applied practice

    6.1: Using algorithms for optimised data collection
    6.2: Automated methods of text analysis and linking of data sets
    6.3: Conclusion and outlook - future possibilities for the Computational Social Sciences

Certificate

For actively participating in the course you will receive an automatic certificate which includes your username, the course name as well as the completed lessons. We want to point out that this certificate merely confirms that the user answered at least 75% of the self-assessment questions correctly.

 

License

This work is licensed under CC BY 4.0

 

Course Instructor

Univ.-Prof. Mag. Dr. Sylvia Kritzinger
Univ.-Prof. Mag. Dr. Sylvia Kritzinger

Professor of Methods in the Social Sciences at the Institute of Government

Sylvia Kritzinger completed her PhD in Political Science at the University of Vienna. She subsequently held post-doctoral positions at Trinity College Dublin and the Institute for Advanced Studies (IHS). She has been a visiting professor at Danube University Krems, at the University of Podgorica in Montenegro, at Keele University and at Trinity College Dublin. Her research focuses on political behaviour, electoral research, democratic representation, political participation and quantitative methods. She is one of the project leaders of the Austrian National Election Study (AUTNES), responsible for 'the Demand Side - Electoral Behaviour', and has led the project 'Representation in Europe'. She was also involved in the 7th Framework Programmes of the European Commission 'PIREDEU' and 'ELECDEM'.

Experts involved in this MOOC:

  • Univ.-Prof. Dipl.-Inform. Dr. Claudia Plant (Professor for Data Mining, Faculty of Computer Science, University of Vienna)
  • Dipl.-Ing. Dr. Axel Böhm (Post Doc Scientist at the Faculty of Mathematics, University of Vienna)
  • Univ.-Prof. Dr. Hajo Boomgaarden (Professor of Methods in the Social Sciences with a focus on Text Analysis at the Department of Communication and Dean of the Faculty of Social Sciences, University of Vienna)
  •  Dr. Anja Eder (Post Doc Lecturer at the Institute of Sociology, University of Graz)
  • Seliem El-Sayed, M.A. (Pre Doc Researcher at the Institute of Political Science, University of Vienna)
  • Ass.-Prof. Mag. Dr. Laurenz Ennser-Jedenastik (Tenure Track for Social Policy at the Institute of Government, University of Vienna)
  • Univ.-Prof. Dr. Nikolaus Forgó (Professor of Technology and Intellectual Property Law at the Faculty of Law, University of Vienna)
  • Univ.-Prof. Dr. Markus Hadler (Professor at the Institute of Sociology, University of Graz)
  • Fabian Kalleitner, MA (Pre Doc Researcher at the Institute for Economic Sociology, University of Vienna)
  • Assoz.-Prof. Dipl-Ing. Dr. Elisabeth Lex (Tenure Track at the Graz University of Technology)
  • Filip Paspalj (pre-doc researcher at the Institute for Innovation and Digitalisation in Law, University of Vienna)
  • Univ.-Prof. Dr. Barbara Prainsack (Professor for Comparative Policy Analysis, University of Vienna)
  • Dr. Dimitri Prandner (Post Doc Researcher at the Department of Empirical Social Research, University of Linz)
  • PhD Nicola Righetti, PhD (Post Doc Scientist at the Department of Communication Studies, University of Vienna)
  • Alexander Seymer, PhD (Senior Scientist at the Department of Sociology and Cultural Studies, Paris Lodron University Salzburg)
  • Martin Teuffenbach, MSc. (Pre Doc Scientist at the Faculty of Computer Science)
  • Ass.-Prof. Dipl.-Ing. Dr. Sebastian Tschiatschek (Tenure Track for Machine Learning, University of Vienna)
  • Univ.-Prof. Dr. Annie Waldherr (Professor for Computational Communication Science at the Department of Communication, University of Vienna)
  • PhD Daniel Weitzel (Post Doc Researcher in the project 'Digitize! at the Institute of Government, University of Vienna)

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