Algorithmic Curation and its Societal Impacts (SoSe 2026)

Opal: Link here.

Time Thursdays, 4. DS

Location VMB/0E02/U

Department Computer Science

Modules INF-BAS3 (Software- und Web-Engineering)

Language English

Assessment oral exam

Description:

Digital platforms increasingly rely on algorithms to organize and curate information. Recommendation systems and ranking algorithms determine which posts appear in social media feeds, which products are suggested in online stores, and which videos or articles gain visibility. These systems therefore play an important role in shaping culture and economy, both online and offline.

This seminar introduces students to the basic computational ideas behind algorithmic curation and explores their broader social consequences. We will study several foundational methods used in modern recommendation systems, including content similarity, clustering, collaborative filtering, and ranking techniques. Through small computational exercises in Python, students will learn how these methods work and how they influence which content becomes visible or popular.

The course also examines how algorithmic curation interacts with social networks and the spread of information. We will discuss how recommendation and ranking systems can create feedback loops that amplify certain content, shape patterns of attention, and influence the diffusion of information online.

Throughout the semester, we connect algorithmic choices and biases to broader societal impacts, such as misinformation, filter bubbles, and polarization. The seminar combines discussion of research papers with short computational exercises and student-led presentations of recent studies.

Students should have basic familiarity with programming in Python or another scripting language, as the course includes small computational exercises. Prior experience with machine learning or recommendation systems is not required; the course introduces the key concepts step by step.

By the end of the course, students will have a practical understanding of how algorithmic curation systems operate, introductory experience with core recommendation systems techniques, and the ability to critically analyze how these systems shape social dynamics online.

Schedule:

Week 1 Introduction: What is Algorithmic Curation?

Week 2 Algorithms as Cultural and Economic Gatekeepers

Week 3 Content Representation, Similarity, and Clustering

Week 4 Collaborative Filtering

Week 5 Ranking and Popularity Bias

Week 6 Evaluating Recommendation Systems

Week 7 Networks and Information Diffusion

Week 8 Algorithmic Bias and Feedback Loops

Week 9 Networked Impact of Algorithmic Biases

Week 10 Governance, Accountability and Alternative Designs

Week 11 Statistical Analysis of Annotation Data (Lecture & Exercises)

Week 12 Student Reading Presentations

Week 13 Final Session: Discussion and Feedback