Introduction to Computational Social Science

Graduate Program (& Advanced Certificate) Status

Course Level: 
Course Open to: 
Students on-site
Academic Year: 
US Credits: 
ECTS Credits: 
Course Code: 
DNDS 6014
Course Description: 

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.

Course schedule
WeekSession title
Brief history of social science and the demand for a quantitative approach. Big Data and new tools. What is CSS? Plan of the course.
2Why CSS?
Complexity of social systems. Data deluge and consequences. Multi-disciplinarity. Problems for CSS.
3Relevant data for CSS?
Types of data, datasets: census, communication (online social networks), work related, online experiments, games. Data collection, cleaning and processing. Some examples.
4Text analysis
What can be learned from automated text analysis? Basic tools, sentiment analysis. Examples (Twitter happiness study, Google n-grams, etc.).
5Networks from data
Weighted, temporal, directed, and bipartite networks. Multilayer networks. Same data – different networks. Node attributes – link attributes. Social network analysis.
6Dynamics of networks
Evolution of social systems from a network perspective. Birth, growth, merger; splitting, shrinkage, death of networks. Role of scales.
7Dynamics of networks
Processes on social networks. Disease spreading, social contagion. How to measure? Data sources, tools, limitations. Examples: Skype studies, Twitter studies.
8Agent based modeling
Basic concepts. Simple models: games, spreading, opinion formation, cultural evolution, and decision models.
9Online experiments
Amazon’s Mechanical Turk. Using Qualtrics. Evaluation of experiments. Examples.
10Burning problems studied by CSS
Fake news, echo chambers, algorithmic bias, wisdom of crowds, cultural diffusion, etc.
11Ethical and legal issues
Privacy issues, GDPR, anonymization, and its limitations. Industrial cooperation. Ownership of data, non-disclosure agreements vs. reproducibility of results.
12Project 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:

- 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

Further information, such as the course website, assessment deadlines, office hours, contact details, etc. will be given during the course.

Learning Outcomes: 
  • Students will learn what computational social science is about, and what are the main techniques and representative research topics.
  • They will get acquainted with some data science tools and databases for studying social systems.
  • They will be able to formulate adequate research questions within computational social science and carry out small scale research projects.
  1. Assessment type 1 (30 % of final grade). Students will get homework consisting of simple problems, which they will have to submit electronically.
  2. Assessment type 2 (60% of final grade). Students will have to prepare independent project work and present it in the last class.
  3. Assessment type 3 (10% of final grade). Students are expected to participate actively in class.

This is an introductory course with no prerequisites. Still, programming and math skills are a plus to take advantage of the course contents:

  • Basic/intermediate programming skills, preferably in Python (e.g. familiarity with variables, loops and conditionals, functions, data structures, file I/O, etc.).
  • Intermediate/advanced math skills: Linear algebra, calculus and differential equations, probability theory and statistics.