46th GESIS Spring Seminar (2017)

Digital Behavioral Data

March 2-20, 2020, Cologne, Germany

The GESIS Spring Seminar (formerly ZA Spring Seminar) has been taking place in Cologne annually for more than 40 years. It offers three consecutive one-week courses in advanced methods of quantitative data analysis for Social Scientists, including a preparatory course in Mathematics for Social Scientists. Language of instruction is English. The Spring Seminar 2020 provides in depth knowledge of digital behavioral data in the social sciences. It enables participants to apply state of the art  techniques to their own research projects and provides participants the opportunity to discuss their own research with interested colleagues and foremost experts.

 

March 2-6, 2020

Lecturers:

Dr. John McLevey, Canada

Jilian Anderson, Canada

Short Course Description:

This course is a hands-on introduction to data analysis in Python for social scientists. It is designed primarily for social scientists who have little to no previous experience with Python, and with varying levels of experience with quantitative and computational data analysis. The course covers a variety of foundational topics related to collecting, cleaning, munging, exploring, and visualizing data. It begins with an introduction to programming in Python for social scientists, covering variables, conditional execution, basic data structures, and functions and methods. It then covers collecting digital behavioural data using web scrapers and application programming interfaces (APIs), cleaning structured and unstructured data, and doing simple data visualizations and exploratory analysis. Upon successful completion of the course, students will have a solid foundation for future learning, including network analysis, natural language processing, and various applications of machine learning algorithms.

March 9-13, 2020

Lecturers:

Dr. Anne Kroon, Netherlands

Dr. Damian Trilling, Netherlands

Short Course Description:

The course will provide insights into the concepts, challenges and opportunities associated with data so large that traditional research methods (like manual coding) cannot be applied anymore and traditional inferential statistics start to lose their meaning. Participants are introduced to strategies and techniques for capturing and analyzing digital data in communication contexts using Python. The course offers hands-on instructions regarding the several stages of computer-aided content analysis. More in particular, students will be familiarized with preprocessing methods, analysis strategies and the visualization and presentation of findings. The focus will be in particular on Machine Learning techniques to analyze quantitative textual data, amongst which both deductive (e.g., supervised machine learning and inductive (e.g., unsupervised machine learning) approaches will be discussed.

March 16-20, 2020

Lecturers:

Dr. David Garcia, Austria

Max Pellert, Austria

Short Course Description:

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.