Social sciences undergo a rapid change. The data deluge due to the fantastic development of computers and information-communication technology opens unprecedented possibilities so that earlier inaccessible problems can be studied and understood, and even predictions can be made. These problems include the large-scale structure of society, multi-scale dynamics of human mobility, social contagion, filtering fake news and identifying echo chambers, urban dynamics and well-being in cities, and many more. The new challenges brought by using Big Data in social sciences require new methods and multi-disciplinary effort. As a result, a new field has emerged based on social science, computer science and complexity science, where computers are pivotal in designing and performing research. Techniques like data mining and processing, agent-based modeling and online experiments, statistical evaluation and theoretical analysis are equally part of it. The goal of the course is to give an introduction to this new, emergent field.
|Brief history of social science and the demand for a quantitative approach. Big Data and new tools. What is CSS? Plan of the course.|
|Complexity of social systems. Data deluge and consequences. Multi-disciplinarity. Problems for CSS.|
|3||Relevant data for CSS?|
|Types of data, datasets: census, communication (online social networks), work related, online experiments, games. Data collection, cleaning and processing. Some examples.|
|What can be learned from automated text analysis? Basic tools, sentiment analysis. Examples (Twitter happiness study, Google n-grams, etc.).|
|5||Networks from data|
|Weighted, temporal, directed, and bipartite networks. Multilayer networks. Same data – different networks. Node attributes – link attributes. Social network analysis.|
|6||Dynamics of networks|
|Evolution of social systems from a network perspective. Birth, growth, merger; splitting, shrinkage, death of networks. Role of scales.|
|7||Dynamics of networks|
|Processes on social networks. Disease spreading, social contagion. How to measure? Data sources, tools, limitations. Examples: Skype studies, Twitter studies.|
|8||Agent based modeling|
|Basic concepts. Simple models: games, spreading, opinion formation, cultural evolution, and decision models.|
|Amazon’s Mechanical Turk. Using Qualtrics. Evaluation of experiments. Examples.|
|10||Burning problems studied by CSS|
|Fake news, echo chambers, algorithmic bias, wisdom of crowds, cultural diffusion, etc.|
|11||Ethical and legal issues|
|Privacy issues, GDPR, anonymization, and its limitations. Industrial cooperation. Ownership of data, non-disclosure agreements vs. reproducibility of results.|
|12||Project presentations by students|
Suggested reading and online resources
- D. Easley and J. Kleinberg, Networks, Crowds and Markets: Reasoning about a Highly Connected World (Cambridge University Press, 2010).
- S. Lehmann and Y. Y. Ahn (Eds.), Complex Spreading Phenomena in Social Systems: Influence and Contagion in Real-World Social Networks (Springer Nature, 2018).
- J. H. Miller and S. E. Page, Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton University Press, 2007).
- M. Newman, Networks: Second Edition (Oxford University Press, 2018).
- A. Pentland, Social Physics: How Good Ideas Spread - The Lessons from a New Science (The Penguin Press, 2014).
- M. J. Salganik, Bit by Bit: Social Research in the Digital Age (Princeton University Press, 2018).
- D. J. Watts, Six Degrees: The Science of a Connected Age (W. W. Norton & Company, 2004).
- D. J. Watts, Everything is Obvious: How Common Sense Fails Us (Crown Business, 2011).
- D. Watts, Computational social science: Exciting progress and future directions. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2013 Symposium (2014). Online at: https://www.nap.edu/read/18558/chapter/6#19
- S. Boccaletti et al., Complex networks: Structure and dynamics. Phys. Rep. 424, 175-308 (2006).
- S. P. Borgatti, A. Mehra, D. J. Brass, G. Labianca, Network analysis in the social sciences. Science 323, 892-895 (2009).
- C. Castellano, S. Fortunato, V. Loreto, Statistical physics of social dynamics. Rev. Mod. Phys. 81, 591-645 (2009).
- R. Conte et al., Manifesto of computational social science. Eur. Phys. J. Spec. Top. 214, 325-346 (2012).
- S. González-Bailón, Social science in the era of big data. Policy & Internet 5, 147-160 (2013).
- D. Lazer et al., Computational social science. Science 323, 721-723 (2009).
- M. W. Macy, R. Willer, From factors to actors: Computational sociology and agent-based modeling. Annu. Rev. Sociol. 28, 143-166 (2002).
- A. Vespignani, Predicting the behavior of techno-social systems. Science 325, 425-428 (2009).
- A. Vespignani, Modelling dynamical processes in complex socio-technical systems. Nat. Phys. 8, 32-39 (2012).
- SocioPatterns datasets
- Stanford Large Network Dataset Collection
- The Colorado Index of Complex Networks
Additional references and online resources will be provided during class.
Further information, such as the course website, assessment deadlines, office hours, contact details, etc. will be given during the course.