Syllabus
- The course can be conducted in English or German (including presentations, pitches, and discussions), depending on the preferences expressed by students in the pre-course survey.
- Regardless of the feedback about the preferred course language, written assignments may be submitted in either German or English, depending on each student’s or group’s preference.
Course description
In this seminar, students are introduced to working with digital behavioral data (DBD). DBD refer to digital traces of human behavior that are knowingly or unknowingly left in online environments (e.g. social media, messengers, entertainment media, or digital collaboration tools). These rich data are increasingly available to social scientific research in the public interest, but can also be used to derive strategic insights for business decisions.
Students learn how to work with DBD alongside the entire research process—from data collection, preprocessing and analysis, to reporting and provision (e.g. via open science tools). Students first get a comprehensive overview of the ways in which DBD can be collected (e.g., API scraping, usage logging, mock-up virtual environments, or data donations), as well as the requirements for data protection, research ethics, and data quality. Afterwards, students apply their newly acquired knowledge in small projects on use cases from media and communication research. In doing so, they learn important computer-based methods with which large digital behavioral data sets (e.g. texts, images, usage behavior logs) can be processed and analyzed. By completing this module, participants will get an up-to-date overview and practical insights into how the potential of observational data (digital traces) can be used to better understand the behavior of media users in digital environments.
Focus of the current course
The seminar in the winter term 2025/26 focuses on the empirical study of outdoor advertising in public spaces—from systematic documentation to content-related and data-based analysis. Participants will develop and test their own research designs to document advertising formats and motives, and explore potential effects. Various methodological approaches can be combined: in addition to designing a short survey instrument (e.g., in the form of an experience sampling design), the seminar involves documenting and describing advertising media (image or text documentation) and analyzing the collected data.
Depending on their research focus, implementation, and data availability, participants may choose quantitative or qualitative approaches—ranging from (visual) content analysis and survey data analysis to geospatial data use. The seminar provides the necessary theoretical foundations and introduces methodological tools such as R or Python.
Learning Objectives
Students will
- overview and understand central opportunities of DBD and accompanying challenges for data collection and preprocessing
- evaluate the strengths and weaknesses of different ways of collecting DBD
- get to know and understand central requirements for data protection, research ethics, and data quality
- get to know and overview key computational social science methods to analyze DBD
- practice and apply knowledge on DBD, statistics, and data analysis in small projects of their own
Recommended prerequisites
- Interest in social scientific perspectives on media, communication, and digital technologies.
- Basic knowledge of working with statistical software such as Stata, R, Python, or SPSS is required.
- Students are recommended, but not required, to also visit the lecture Data Science: Foundations, Tools, Applications in Socio-Economics and Marketing.
Organization of the course
Registration for the course takes place via StudOn. There you will receive the first information and instructions. Please make sure to complete the short pre-course survey before the seminar begins.
All slides, assignment instructions, an up-to-date schedule, and other course materials may be found on the course website. I will regularly send out course announcements by e-mail, so please make sure to check your mail address associated with StudOn regularly.
(Preliminary) Schedule
For the latest, more detailed version of the course schedule as well as the linked content of the individual sessions (e.g. slides or literature for the respective presentation), please see Schedule.
| Session | Datum | Time | Session Type | Topic | Room |
|---|---|---|---|---|---|
| 1 | 24.10.2025 | 09:45 - 11:15 | 📖 Single Session | Kick-Off | 2.024 (FG) |
| 2 | 29.01.2026 | 09:00 - 16:00 | 📚 Block Session | From Theory to Questionnaire | 2.024 (FG) |
| 3 | 23.01.2026 | 09:00 - 16:00 | 📚 Block Session | From Questionnaire to the Field | 2.024 (FG) |
| 4 | 30.01.2026 | 09:00 - 16:00 | 📚 Block Session | From Field to Analysis | 2.024 (FG) |
| 5 | 06.02.2026 | 09:00 - 16:00 | 📚 Block Session | Summary & Evaluation | 2.024 (FG) |
Information about the sessions
📖 Single Session
The kick-off session provides an introduction to DBD and presents the course topic and organization.
📚 Block Sessions
The block sessions are longer sessions (one full day) that allow for more in-depth work on specific topics. The block sessions will include presentations, discussion, and practical work in the form of group activities and exercises.
The goal of the sessions is to be as interactive as possible. My role as the instructor is to introduce new tools and techniques, but it is up to you to apply and explore them. Much of your work in this course will involve creating questionnaires, testing survey tools, and writing code.
As coding is a skill that is best learned by doing, you are expected to bring a laptop to the sessions were we are “live-coding” so that you can take part in the in-session exercises. Please make sure your laptop is fully charged before you come to class as the number of outlets in the classroom will not be sufficient to accommodate everyone.
Where to ask questions
- If you have a question during the lecture, feel free to ask it! There are likely other students with the same question, so by asking you will create a learning opportunity for everyone.
- Any general questions about session content, assignments, or about the project should be posted into the
StudOn-Forum, so that everyone can benefit from the answers. There is a chance another student has already asked a similar question, so please check the other posts before adding a new question. If you know the answer to a question, I encourage you to respond!
- E-mails should be reserved for personal matters.
Assessment
In order to obtain credits and a grade, participants are required to
- attend regularly (at least 80% of the sessions) and participate actively. A maximum of two sessions can be missed without excuse. This translates to one morning or afternoon of a block session. Absences beyond this limit can only be excused in cases of illness (i. e. with a medical certificate).
- complete various assignments as part of a portfolio. The type and scope of assignments depend on the number of participants and the project(s). Detailed information can be found in the section Assignments.
Academic integrity
Do not cheat!
For general information on formatting, style, citation, appendices, and the wording of the affidavit, see our Guide to Academic Writing.
Policy on sharing and reusing code
I am well aware that a huge volume of code is available on the web to solve any number of problems. Unless I explicitly tell you not to use something, the course’s policy is that you may make use of any online resources (e.g. StackOverflow) but you must explicitly cite where you obtained any code you directly use (or use as inspiration). Any recycled code that is discovered and is not explicitly cited will be treated as plagiarism.
Policy on use of generative artificial intelligence (AI):
You should treat generative AI, such as ChatGPT, the same as other online resources. There are two guiding principles that govern how you can use AI in this course1: (1) Cognitive dimension: Working with AI should not reduce your ability to think clearly. We will practice using AI to facilitate—rather than hinder—learning. (2) Ethical dimension: Students using AI should be transparent about their use and make sure it aligns with academic integrity.
✅ AI tools for code: You may make use of the technology for coding examples on assignments; if you do so, you must explicitly cite where you obtained the code. Any recycled code that is discovered and is not explicitly cited will be treated as plagiarism. You may use these guidelines for citing AI-generated content.
❌ AI tools for narrative: Unless instructed otherwise, you may not use generative AI to write narrative on assignments. In general, you may use generative AI as a resource as you complete assignments but not to answer the exercises for you. You are ultimately responsible for the work you turn in; it should reflect your understanding of the course content.
Recommended textbooks
Atteveldt, W. van, Trilling, D., & Arcíla, C. (2021). Computational analysis of communication: A practical introduction to the analysis of texts, networks, and images with code examples in python and r. John Wiley & Sons.
Engel, U., Quan-Haase, A., Liu, S. X., & Lyberg, L. (2021). Handbook of Computational Social Science, Volume 1: Theory, Case Studies and Ethics (1st ed.). Routledge. https://doi.org/10.4324/9781003024583
Engel, U., Quan-Haase, A., Liu, S. X., & Lyberg, L. (2021). Handbook of computational social science, volume 2. Routledge. https://doi.org/10.4324/9781003025245
Haim, M. (2023). Computational Communication Science: Eine Einführung. Springer Fachmedien Wiesbaden. https://link.springer.com/10.1007/978-3-658-40171-9
Salganik, M. J. (2018). Bit by bit: Social research in the digital age. Princeton University Press.
Footnotes
These guiding principles are based on Course Policies related to ChatGPT and other AI Tools developed by Joel Gladd, Ph.D↩︎