The continuously growing importance of the internet for everyday life and the correspondingly increasing volume of digital behavioral data on the web allow us to study human behavior from new perspectives. However, accessing or collecting such data is not always straightforward. Moreover, the heterogeneity of collected data poses the challenge of data pre-processing, ensuring that they can be effectively used in further analyses. Thus, this course aims to introduce participants to data collection from online platforms and the pre-processing necessary to make it usable for their research. Apart from these essential, technical foundations, we will also discuss basic methods to enrich raw, textual data with additional features. Lastly, we will present participants with a framework for the critical reflection on their data collection processes and documentation of their data.

 

This course will show and teach participants how content, comment, and interaction data can be automatically collected from social media platforms (e.g., YouTube, Reddit) or other online platforms (e.g., eBay, Amazon). We will cover the main aspects of collecting data using the programming language Python, including APIs and custom scrapers for static and dynamic web pages. We will also show how collected data can be cleaned, pre-processed, and curated to enable further statistical analyses.

 

The course will include lectures on each topic, introducing the basic theoretical concepts necessary for understanding the practical implementations, which are then presented in live-coding sessions and practiced during exercises. The exercises will be conducted individually or in small groups and assisted by the instructors, who help with questions and problems. In mini-projects, participants have the chance to discuss how they can apply and integrate the newly learned methods within their research or personal projects.

 

For additional details on the course and a day-to-day schedule, please download the full-length syllabus.