Opal: Link here.
Department: Sociology
Language: English
Termin: Do (4)
Raum: FAL 07/08
Assessment: report
Social media generates new types of data for quantifying human behavior, but also raises interesting research questions about the interaction between technology and society. In this course, we will learn the basics of computational social science and apply its methods in hands-on exercises to real-world data science problems from social media and other sources.
Opal: Link here.
Time Thursdays, 5. DS
Location BAR/0I89
Department Computer Science
Modules INF-BAS3 (Software- und Web-Engineering)
Language English
Assessment oral exam
Online platforms generate vast amounts of behavioral data, including subjective ratings (e.g., likes, stars, reactions) and objective engagement metrics (e.g., views, clicks, watch time). While machine learning excels at predicting user behavior, statistical modeling—a cornerstone of empirical research—is critical for interpretable analysis. It helps uncover relationships between user behavior and various factors (e.g., how demographics influence engagement patterns) and enables causal inference in experiments (e.g., measuring the impact of different feed-ranking algorithms on user experience). These methods are widely used in UX research, product analytics, and A/B testing.
Opal: Link here.
Time (Lecture) Mondays, 3. DS and 5. DS
Location APB/E001
Department Computer Science
Modules INF-BAS3 (Software- und Web-Engineering)
Language English
Assessment oral exam
The lecture will introduce you to social science research questions and findings, while the exercise will give you the practical skills to answer social questions yourself using computational tools. The first part focuses on basic techniques of text analysis. We will learn how to collect text data from sources such as newspapers, parliamentary minutes or social media, and how to pre-process it for natural language processing (NLP), such as sentiment analysis.