GESIS Training Courses

Wiss. Koordination

Kathrin Busch
Tel: +49 221 47694-226

Administrative Koordination

Angelika Ruf
Tel: +49 221 47694-162

Week 3: Social Network Analysis with Digital Behavioral Data

Dr. David Garcia, Max Pellert

Datum: 16.03 - 20.03.2020 ics-Datei

Veranstaltungsort: Cologne / Course language: English

Referenteninformationen - Dr. David Garcia

Referenteninformationen - Max Pellert


Social Network Analysis with Digital Behavioral Data provides a broad approach to the quantitative analysis of social networks and social interaction through digital trace data. The course integrates the implementation of data retrieval and processing, the application of statistical analysis methods, and the interpretation of results to derive insights of human behavior at high resolutions and large scales. The motivation of the course stems from theories in the Social Sciences, which are addressed with respect to societal phenomena and formulated as principles that can be tested against empirical data. The course includes the retrieval of digital behavioral data in an automated manner, using Twitter and other resources and programming interfaces to capture social network data. This is followed by methods for processing this data to construct social networks and to process the content of social interactions. Exercises sessions are linked and allow the students to analyze a sample of Twitter users of their interests over the length of the course.



Participants will find the course useful if they
  • are social scientists aiming to use digital behavioral data to inform social theory
  • have interest on learning about online phenomena and the digital society
  • want to learn how to analyze social data with R
  • want to retrieve their own datasets of online social networks


By the end of the course participants will
  • understand techniques to retrieve digital trace data from online data sources
  • store, process, and summarize online social network data for quantitative analysis
  • perform statistical analyses to test hypotheses, derive insights, and formulate predictions
  • implement streamlined software that integrates data retrieval, processing, statistical analysis, and visualization of social networks
  • interpret the results of data analysis with respect to theoretical and testable principles of human behavior
  • understand the limitations of observational data analysis with respect to data volume, statistical power, and external validity


  • Knowledge of basic statistics (moments, distributions, correlation)
  • Basic programming knowledge (variables, loops, conditions)
  • Basic algebra and calculus


Weitere Informationen