Con­cepts in So­cial Data Sci­ence

Course content

This course introduces the core concepts, theories, and methodological approaches of social data science, with a focus on understanding digital behaviour in online environments and applying these insights to real organisational and societal challenges.

Students learn how to work with diverse forms of social data to investigate social processes such as influence, trust, emotion, networks, information flow, and AI-mediated interaction, and to understand how these processes shape behaviour in a range of real-world settings. The course also examines how artificial intelligence shapes the production, structure, and interpretation of social data, as well as the social environments in which this data is generated.

The course combines conceptual foundations from the social sciences with hands-on analysis skills, anchored in real-world problems and applications. Students learn how to collect, model, and analyse social data, and how to interpret results in theoretically meaningful and practically relevant ways.

Emphasis is placed on connecting methods to substantive social questions rather than treating tools in isolation. By the end of the course, students will be able to design and execute end-to-end social data analyses and translate insights into organisational and societal value.

Course contents address how to create, handle, analyse, and interpret social data for applied use. Topics will include:

• Foundations of Social Data Science
• Digital Behaviour, Influence, and Social Contagion
• Social Network Analysis
• Natural Language Processing and Language-Based Data
• Agent-Based Modelling of Social Systems
• Social Lab Experiments and Field Experiments
• Artificial Intelligence, Algorithmic Environments, and Information Flows
• Ethics and Governance of Social Data