Digital Behavioral Data

Winter semester 2022/23

Author

Affiliation

Christoph Adrian

Chair of Communication Science

Contents

In this seminar, students will be introduced to working with digital behavioral data (DBD). DBD refers 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 will 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 will 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 will practice and apply their newly gained knowledge in small projects on use cases from media and communication research. In doing so, they learn about key computational methods via which large digital behavioral datasets (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 to harness the potential of observational data traces to better understand media users’ behavior in digital environments.

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.

Schedule

Below is the scheudle for the current semester.

Session	Datum	Topic
1	26.10.2022	Kick-Off Session
2	02.11.2022	DBD: Introduction & Overview
3	09.11.2022	DBD: Data collection process in focus
4	16.11.2022	API-Access (I): Twitter
5	23.11.2022	API-Access (II): YouTube
6	30.11.2022	API-Access (III): Reddit
7	07.12.2022	Webscraping: TikTok
8	14.12.2022	Extra: Text as data
-	-	CHRISTMAS BREAK
9	11.01.2023	ESM: m-path
10	18.01.2023	Data Donations
11	25.01.2023	Guest Lecture: Linking DBD & Survey data
12	02.02.2023	Bring Your Own Research (Project)
13	08.02.2023	Closing Session: Recap, Evaluation & Discussion

Copyright

This content is licensed under a GPL-3.0 License.

References

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. (2021a). 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. (2021b). Handbook of computational social science, volume 2. Routledge. https://doi.org/10.4324/9781003025245

GESIS - Leibniz-Institut für Sozialwissenschaften. (n.d.). Digitale Verhaltensdaten. https://www.gesis.org/institut/digitale-verhaltensdaten

Salganik, M. J. (2018). Bit by bit: Social research in the digital age. Princeton University Press.