Fall 2016 course was organized at University of Helsinki, Faculty of Social Science.

Course goal

After finishing the course, students understand the background of computational social science. They can identify different methods used in the field and recognize the suitable approaches for a novel research problem. They are familiar with practical challenges related to the research, e.g., in ethics.

Course operations

  • Lectures are not mandatory but the teacher make no guarantees that the slides make sense without being present in the lecture (i.e.; I'll aim to make presentation slides) nor can the student join the intellectual discussions around the theme. This said, the material mostly resists in articles linked to each week.
  • Pre-reading / post-reading includes some key conceptual articles on this domain, aiming to deepen the knowledge and allow more boundary crossing discussions.
  • Lecture diary is used to reflect the lecture ideas (that is: try to input your own thinking and not just summarise what I've told). The length each should be less than 300 words. The lecture diaries must be written after the lecture on Tuesdays and they will be peer- and teacher commented/discussed before Thursday. If you have any questions from the previous week, add those as the final section of your lecture diary.
  • Research plan proposes a project around computational social science. Use the Academy of Finland format for your research plan (no budgeting, no CVs or personal data). Max 12 pages, per Academy of Finland guideline (including references).


Introduction (September 9th)

  • Course practices and organisation
  • Perspectives on computational social science

Pre-reading (choose 2)

  • Grimmer. 2015. We Are All Social Scientists Now: How Big Data, Machine Learning, and Causal Inference Work Together.
  • Nelimarkka. 2014. Laskennallinen yhteiskuntatiede (Bachelor's thesis) - sorry, in Finnish only
  • Cioffi-Revilla. 2010. Computational Social Science.
  • Lazer et al. 2009. Life in the network: the coming age of computational social science
  • Conte et al. 2012. Manifesto of Computational Social Science

Additional readings (based on lecture slides and lecture)

  • Raento, M., Oulasvirta, A., & Eagle, N. (2009). Smartphones. An emerging tool for social scientists. Sociological Methods & Research, 37(3), 426–454.
  • van Wijk, J. (2006). Bridging the Gaps. Computer Graphics and Applications, IEEE, 26(6), 6–9.
  • Haverinen, A. & Suominen, J. (2015) Koodaamisen ja Kirjoittamisen vuoropuhelu? Mitä on digitaalinen humanistinen tutkimus. Ennen ja nyt.

Data Science (16th September)

  • What is data science?
  • Methods
  • Big data

Pre-readings (choose 1)

  • Schwartz, H. A., & Ungar, L. H. (2015). Data-Driven Content Analysis of Social Media: A Systematic Overview of Automated Methods. The ANNALS of the American Academy of Political and Social Science, 659(1), 78–94.
  • Gayo-Avello, D. (2013). A Meta-Analysis of State-of-the-Art Electoral Prediction From Twitter Data. Social Science Computer Review, 31(6), 649–679.

Motivating questions

  • What kind of approaches are there for doing 'data science' (assuming these articles do it)?
  • How would you group them?

Additional reading

  • Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 37–54.

Network analysis (23rd September)

  • Reflecting actors and ties - what forms a network?
  • From descriptive to computational network analysis
  • Key jargon


  • Everyone: Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. science, 323(5916), 892-895.
  • Choice: Borgatti, S. P., & Foster, P. C. (2003). The network paradigm in organizational research: A review and typology. Journal of management, 29(6), 991-1013. (pages 993-999 only, more empirical)
  • Choice: Borgatti, Stephen P., and Daniel S. Halgin. "On network theory." Organization Science 22.5 (2011): 1168-1181. (pages 1175-1179, more theoretical)

Motivating questions

  • What are ties, nodes, structure?
  • Authors constantly make references back to physics. Why do you think they make it so? Do those justify social networks more?
  • You can mostly skim the history; just observe there's plenty of it.
  • Why networks benefit the research?

Simulation studies (September 30th)

  • Few classical simulation models
  • How to choose parameters?


  • Skim Chapters 1 and 2 from Gilbert G. Nigel, Troitzsch Klaus G. (2005). Simulation for the social scientist. Open University Press. [University of Helsinki link]

Focus to...

  • to understand what type simulations are there
  • what is the process of simulation like?

Complex systems and modelling (7th October)

  • Thinking in complex systems way
  • Modelling as activity
  • Why to apply complexity and why not?


  • Gerrits, L., & Marks, P. (2015). How the complexity sciences can inform public administration: an assessment. Public Administration, 93(2), 539-546.
  • Lansing, J. S. (2003). Complex adaptive systems. Annual review of anthropology, 183-204.

Motivating questions

  • check that you get the key characters of complexity
  • read and think about an application domain

Process and other apects of computational research

  • Process of computational research
  • Validity
  • Ethics


Everyone reads

  • boyd, d., & Crawford, K. (2012). Critical Questions for Big Data. Information, Communication & Society, 15(5), 662–679.

Read one (validation)

  • Hindman, M. (2015). Building Better Models: Prediction, Replication, and Machine Learning in the Social Sciences. The ANNALS of the American Academy of Political and Social Science, 659(1), 48–62.
  • Grimmer, J., & Stewart, B. M. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(3), 267–297.
  • Carter, D., & Sholler, D. (2015). Data science on the ground: Hype, criticism, and everyday work. Journal of the Association for Information Science and Technology, (2013), n/a-n/a.
  • Villatoro, D., Andrighetto, G., Brandts, J., Nardin, L. G., Sabater-Mir, J., & Conte, R. (2013). The Norm-Signaling Effects of Group Punishment: Combining Agent-Based Simulation and Laboratory Experiments. Social Science Computer Review, 32(3), 334–353.

Read one (power)

  • Bucher, T. (2016). ’Machines dont have instincts: Articulating the computational in journalism. New Media & Society.
  • Gillespie, T. (2012). The relevance of algorithms. In Media Technologies: Essays on Communication, Materiality, and Society (pp. 167–194).
  • Saunders-Newton, D., & Scott, H. (2001). “But the Computer Said!”: Credible Uses of Computational Modeling in Public Sector Decision Making. Social Science Computer Review, 19, 47–65.


  • Think of problems related to research and post them to Presemo by Thursday.

Summary and review (October 21st)

8.15 - 9.00 Peer-review of Research Proposals. To attend, upload them here.

9.00 - 10ish Summary and review, course evaluation, feedback discussion

Return of final assigment

5th November (Saturday, 8pm)