Introduction to Computational Social Sciences
Opal: Link here.
Department: Sociology
Language: English
Termin: Do (4)
Raum: FAL 07/08
Assessment: report
Description:
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.
The first part will be more theoretical to establish the tools and vocabulary needed for application, starting with descriptive statistics over dynamical processes to network science, but also data collection from social media and field experiments conducted online. 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, such as sentiment analysis.
We will then have the tools to move on to applied examples ranging from social network data to text data to experimental data, covering topics like political polarization, misinformation, public-health and human mobility. We will learn how to use data science to describe these different datasets using the same language and what conclusions can be drawn from these descriptions in terms of research questions.
Finally, we will go beyond description and learn about causality, simulation models of social systems that allow mechanistic understanding. In summary, this course will provide a toolbox to describe different types of social systems, as well as a family of modeling approaches that can go beyond individual-level statistics and describe collective processes.
Schedule:
Week 1: What is Computational Social Science?
Week 2: Data, types, sources and preparation in R/Python
Week 3: Exploring data structure with descriptive analysis.
Week 4: Describing temporal data/time series analysis.
Week 5: Predicting outcomes with regression.
Week 6: Relational data 1: graph theory + empirical networks.
Week 7: Relational data 2: visualization + random networks.
Week 8 : Excursion: Digital media and Democracy
Week 9: Causal inference in observational data
Week 10: Agent-based-models
Week 11: Randomized Control Trials
Week 12: Field experiments on and about social media
Week 13: Current trends in Computational Social Science
Week 14: Final session, feedback, Q&A, Discussion
Bibliography:
Llaudet, E., & Imai, K. (2022). Data analysis for social science: A friendly and practical introduction. Princeton University Press.
Albert-László Barabási (2015). Network Science. Cambridge University Press (also here: networksciencebook.com)
Cunningham, S. (2021). Causal inference: The mixtape. Yale university press.
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A. L., Brewer, D., … & Van Alstyne, M. (2009). Computational social science. Science, 323(5915), 721-723.
Flache, A., Mäs, M., Feliciani, T., Chattoe-Brown, E., Deffuant, G., Huet, S., & Lorenz, J. (2017). Models of social influence: Towards the next frontiers. Jasss-The journal of artificial societies and social simulation, 20(4), 2.
Lorenz-Spreen, P., Oswald, L., Lewandowsky, S., & Hertwig, R. (2023). A systematic review of worldwide causal and correlational evidence on digital media and democracy. Nature human behaviour, 7(1), 74-101.
