Teaching Offer

At SynoSys (Center Synergy of Systems) we combine latest research with innovative teaching methods to provide students with a dynamic and interdisciplinary learning experience. Our research-driven courses cover key topics such as Data Visualization, complex systems, and computational social science.

Through interactive seminars, project-based learning, and critical discussions, we empower students to actively engage with scientific processes and develop essential skills for data-driven research and innovation. Our teaching philosophy emphasizes hands-on approaches, collaboration with leading researchers, and the application of real-world insights.

Find more information about our latest seminars and courses, opportunities for student research projects and access to teaching materials and resources.

Join us and explore how academic research meets practical application in a rapidly evolving digital world.

Table of contents

1. 1. 1. Methods to Computational Social Sciences (CSS)
      2. Introduction to Statistical Modeling for Online Behavior Data (Seminar)

Methods to Computational Social Sciences (CSS)

Dr. Philipp Lorenz-Spreen (Lecture)

Dr. Sami Nenno (Exercise) 

Time (Lecture)	Mondays, 3. DS
Location (Lecture)	APB/E001
Time (Exercise)	Mondays, 5. DS
Location (Exercise)	APB/E010
Type/SWS	(2/2/0)
Modules	INF-BAS3 (Software- and 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 sentiment analysis. 

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. 

The last part of the course will focus on NLP again, this time with a focus on deep learning methods such as Large Language Models (LLMs). We will use transformer models to identify complex patterns in the communication of German parties on social media. 

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.

Enrollment: 

• Official enrollment (e.g., for exam): SELMA
• Communication and learning materials: OPAL
• Enrollment to both is open until 07-31-2025

Schedule:

Week #	Date	Topic
Week 1		What is Computational Social Science?
Week 2		Data, types, sources and preparation in R/Python
Week 3		Collecting text data
Week 4		Identifying sentiment and clusters
Week 5		Time series analysis
Week 6		Regressions and statistical modeling
Week 7		Network science 1
Week 8		Network science 2
Week 9		Causal inference in observational data
Break	June 8 - 15
Week 10		Agent-based-models
Week 11		Topic Modeling with Transformers 1
Week 12		Topic Modeling with Transformers 2
Week 13		LLMs
Week 14		Final session, feedback, Q&A, Discussion

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.

Introduction to Statistical Modeling for Online Behavior Data (Seminar)

Dr. Kamil Fuławka 

Time	Thursdays, 5. DS
Location	BAR/0I89
Type/SWS	(0/0/2)
Modules	INF-BAS3 (Software- und Web-Engineering)
Language	English
Assessment	oral exam

Description:

Online platforms generate vast amounts of behavioral data, including subjective ratings (e.g., likes, stars, reactions) and objective engagement metrics (e.g., views, clicks, watch time). While machine learning excels at predicting user behavior, statistical modeling—a cornerstone of empirical research—is critical for interpretable analysis. It helps uncover relationships between user behavior and various factors (e.g., how demographics influence engagement patterns) and enables causal inference in experiments (e.g., measuring the impact of different feed-ranking algorithms on user experience). These methods are widely used in UX research, product analytics, and A/B testing.

This course provides a hands-on introduction to statistical modeling in R, focusing on methods most useful for analyzing behavioral data. We'll cover key concepts and progress to model selection based on outcome type. Topics include logistic regression for binary outcomes (e.g., like/dislike reactions), ordinal regression for rating scales, beta regression for continuous feedback (e.g., sliders), and hierarchical models for nested data (e.g., multiple ratings from the same users). In the end, you'll not only know which model to use but also how to effectively visualize and communicate your findings through clear, interpretable result presentations.

No prior statistical modeling experience is required. If you're familiar with Python or similar languages, the transition to R should be easy. All scripts and exercises are provided.

Enrollment: 

• Official enrollment (e.g., for exam): Selma
• Communication and learning materials: OPAL
• Enrollment to both is open until 07-31-2025

Schedule:

Part 1: General Introduction

Week #	Date	Topic
Week 1		Introduction to statistical modeling: goals and principles
Week 2		Linear regression: the foundational model, applications and limitations

Part 2: Selected Generalized Linear Models

Week #	Date	Topic
Week 1		Logistic regression 1: analysis of likes and dislikes
Week 2		Logistic regression 2: multivariate analysis
Week 3		Ordinal regression 1: stars and other likert-type ratings
Week 4		Ordinal regression 2: modeling distributional parameters
Week 5		Beta regression 1: slider-type ratings
Week 6		Beta regression 2: modeling distributional parameters
Week 7		Interim summary, additional exercises, and other GLMs

Part 3: Experimental design & hierarchical models

Week #	Date	Topic
Week 1		Modeling between- and within-individual experimental designs
Week 2		Hierarchical models 1: introduction
Week 3		Hierarchical models 2: logistic and ordinal regression
Week 4		Hierarchical models 3: Beta regression
Week 5		Final session, Q&A, discussion and feedback