Methods for Computational Social Sciences (SoSe 2026)

Opal: Link here.

Time (Lecture) Mondays, 4. DS and (Exercise) 5. DS

Location VMB/0302 (Lecture), APB/E010 (Exercise)

Department Computer Science

Modules INF-BAS3 (Software- und Web-Engineering)

Language English

Assessment oral exam

Description:

Social media generates new types of data for quantifying human behavior, but also raises interesting research questions about the interaction between technology and society. In this course, we will learn the basics of computational social science, with a focus on text-as-data, and apply its methods to real-world data science problems from social media and other sources.

The lecture will introduce you to social science research questions and findings, while the exercise will give you the practical skills to answer social questions yourself using computational tools. The first part focuses on basic techniques of text analysis. We will learn how to collect text data from sources such as newspapers, parliamentary minutes or social media, and how to pre-process it for natural language processing (NLP), such as topic modeling and classification.

We will then move on to analyzing the structural factors of online data, from time series to networks and their dynamics. We will also explore how to collect such data from social media and how to conduct online field experiments. We will then have the tools to move on to applied research questions, covering topics such as political polarization, misinformation, public health and human mobility. We will learn what conclusions can and cannot be drawn from these descriptions. Finally, we will go beyond description and learn about causality, simulation models of social systems that allow mechanistic understanding.

In summary, this course will provide a toolbox for describing different types of social systems with a focus on textual data, as well as a family of modelling approaches that can go beyond individual-level statistics to describe collective processes.

Schedule:

Week 1 Introduction

Week 2 Collecting text data

Week 3 Automated content analysis, validation & data quality I

Week 4 Automated content analysis, validation & data quality II

Week 5 Transfer learning & zero/few shot learning

Week 6 Vector embeddings & topic modeling I

Holiday

Week 7 Vector embeddings & topic modeling II

Week 8 Time series analysis

Week 9 Regressions and statistical modeling

Week 10 Network science I

Week 11 Network science II

Week 12 Causal inference in observational data

Week 13 Linking data types (time, structure and content)

Week 14 Linking data types (time, structure and content)

Bibliography:

Llaudet, E., & Imai, K. (2022). Data analysis for social science: A friendly and practical introduction. Princeton University Press.

Albert-László Barabási (2015). Network Science. Cambridge University Press (also here: networksciencebook.com)

Cunningham, S. (2021). Causal inference: The mixtape. Yale university press.

Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A. L., Brewer, D., … & Van Alstyne, M. (2009). Computational social science. Science, 323(5915), 721-723.

Flache, A., Mäs, M., Feliciani, T., Chattoe-Brown, E., Deffuant, G., Huet, S., & Lorenz, J. (2017). Models of social influence: Towards the next frontiers. Jasss-The journal of artificial societies and social simulation, 20(4), 2.

Lorenz-Spreen, P., Oswald, L., Lewandowsky, S., & Hertwig, R. (2023). A systematic review of worldwide causal and correlational evidence on digital media and democracy. Nature human behaviour, 7(1), 74-101.