This course introduces students to the field of computational social science. It covers four core research areas in the field: automated data extraction, social complexity, computational simulations and social network analysis. Each topic is first briefly introduced conceptually and then relevant foundational and applied research papers are discussed. Selected readings accompany each section and additional recommended readings are provided that allow students to delve deeper into selected topics. Where possible, relevant data and applications are illustrated with practical hands-on examples. The objective of the course is to familiarize students with quantitative approaches at the intersection of social science research and computational methodologies while reflecting on their strengths and limitations. As part of the seminar, students develop the concept for a research design using computational social science methodologies. The course is a conceptual introduction, no specific technical skills are required.
The lectures take place at the Jinonice Campus of Charles University in the Department of Security Studies, Faculty of Social Sciences. We meet in room J2063 on:
- Wednesday Feb 28, 9:30 – 11:00 & 12:30 – 14:00
- Thursday Mar 1, 9:30 – 11:00 & 12:30 – 14:00
- Friday Mar 2, 9:30 – 11:30
The students’ performance in the course will be assessed based on these criteria:
- Submission of Research Design (90 %)
- Attendance/Activity (10 %)
As part of the course, students are required to develop the concept for a research design using computational social science methodologies applied to a substantial topic of their choice. Initial ideas have to be presented in the last session of the course and a full 5 to 10-page research design has to be submitted 2 weeks after the course.
All students are required to participate in each session. One unexcused absence will be tolerated, more absences will be considered on an individual basis.
Session 1: Introduction to Computational Social Science
The introductory lesson will give a brief overview of the course, the formalities and then provides an introduction to the field of Computational Social Science and the use of novel quantitative methods in the study of social phenomena.
- Lazer, David, Alex Pentland, Lada Adamic, Sinan Aral, Albert-L szl Barabasi, Devon Brewer, Nicholas Christakis, Noshir Contractor, James Fowler, Myron Gutmann, Gary King, Michael Macy, Deb Roy and Marshall Van Alstyne. (2009). Computational Social Science. Science 323: 721–723.
- Giles, Jim. (2012). Computational Social Science: Making the Links. Nature 488: 448–450.
Session 2: Automated Data Extraction & Social Media Data
One of the key applications of computational methodologies pertains to the automated collection of (online) data. This session first reviews classical approaches to systematic collection of relevant data from news media articles and political texts and its processing. It then covers recent approaches using the vast volume of data on human interactions on social media platforms.
- Grimmer, Justin and Brandon M. Stewart. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis 21(3): 267–297.
- Earl, Jennifer, Andrew Martin, John D. McCarthy and Sarah A. Soule. (2004). The Use of Newspaper Data in the Study of Collective Action. Annual Review of Sociology 30(1): 65–80.
- Golder, Scott A., and Michael W. Macy. (2011). Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures. Science 333: 1878–1881.
- Ruths, Derek, and J rgen Pfeffer. (2014). Social Media for Large Studies of Behavior. Science 192: 59–60.
Session 3: Social Complexity and Agent-Based Simulations
The study of social complexity lies at the very foundation of early quantitative approaches to social science research. This session, first reviews key concepts of complexity and complex systems. It then turns to a discussion of how agent-based models can be used to capture complex endogenous dynamics in such systems, including recent approaches that go beyond abstract conceptual models and seed, calibrate and validate their simulations with empirical data.
- Epstein, Joshua M. (1999). Agent-Based Computational Models and Generative Social Science. Complexity 4(5): 41–60.
- Miller, John H. and Scott E. Page. (2004). The Standing Ovation Problem. Complexity 9(5): 8–16.
- Schelling, Thomas C. (1971). Dynamic Models of Segregation. Journal of Mathematical Sociology 1: 143–186.
- Bhavnani, Ravi, Karsten Donnay, Dan Miodownik, Maayan Mor, and Dirk Helbing. (2014). Group Segregation and Urban Violence. American Journal of Political Science 58(1): 226–245.
Session 4: Social Network Analysis
The study of social networks has a long tradition in the social sciences. Through the advent of social media platforms, much larger and more comprehensive data on social networks ties have become available that have revolutionized the way in which social network analysis can aid in the study of relevant social phenomena. This session first reviews the historical development of the field and then discusses recent approaches and quantitative studies.
- Lazer, David. (2011). Networks in Political Science: Back to the Future. PS: Political Science & Politics 44(1): 61–68.
- Conover, Michael D., Jacob Ratkiewicz, Matthew Francisco, Bruno Goncalves, Alessandro Flammini and Filippo Menczer. (2011). Political Polarization on Twitter. Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, p. 89–96.
- Coppock, Alexander, Andrew Guess and John Ternovski. (2016). When Treatments are Tweets: A Network Mobilization Experiment over Twitter. Political Behavior 38(1): 105–128.
- Bisbee, James and Jennifer M. Larson. (2017). Testing Social Science Network Theories with Online Network Data: An Evaluation of External Validity. American Political Science Review 111(3): 502–521.
Session 5: Student Presentations
In this session, students give short (5 min) presentations of an initial idea for a research design incorporating computational social science methodologies to address a substantial question of their choice. Each presentation will be followed by a short in-class discussion providing feedback and ideas.