Fall 2017

The course will introduce the emerging field of computational social science (as discussed in Cioffi-Revilla, 2010). The aim is to provide an overview of potential methods facilitated by the access to computational resources.

Course practices

Pre-readings is my approach to ask you to engage to topics before coming to the classrooms. The article provide background, ideas and key terms which we extend and elaborate during the classes. We will also occasionally discuss about these articles and their content. To help serving in these purposes, some of the lecture diary activites relate to pre-readings.

Lectures are not mandatory but I 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.

Each student writes learning diary

  1. Before each lecture where you point out what you find interesting in the articles, what you find problematic and reflect the key points in the articles. Matti will suggest some topics to consider on.
  2. After each lecture to summarize what was the key take-home-message of the lecture and any open questions after the lecture.

Note that the key point of learning diary for me is to allow you reflect and think aloud things. Try to avoid repeating what was said and rather weight your own opinions. We will use an online platform for these discussions and students are expected to comment the after-lecture notes by providing their insights and comments to the posts.

I urge you to limit the length of learning diary to 300 words just to give an idea of the size of contribution (which is small) in these learning diaries. To foster the interaction, it is mandatory to respond to at least one after-lecture notes (and I will also engage the discussion). If you miss a class, kindly write at least three comments to the posts. (No need to write an after-lecture summary, naturally.)

As an final assigment, you will write a research proposal where they apply the methods or concepts presented in this course to a research question or problem they find interesting. This consists of a research plan and a data management plan. Follow the Academy of Finland guidelines on content, length etc. For budjeting the research proposal use the a dummy research budjet template (to be added).

Important links

Course evaluation

20% of the grade is based on the lecture diary.

80% of the grade is based on the quality and innovativeness of the research proposal. I will use the Academy of Finland review form.


Lectures 1-2: Introduction and ethics

Goal of lecture

  1. Course practices and organization
  2. What is computational social science?
  3. Validity
  4. Ethics

What is computational social science (read two)

  • Grimmer. 2015. We Are All Social Scientists Now: How Big Data, Machine Learning, and Causal Inference Work Together.
  • 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

Validation (read one)

  • 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.
  • 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.

Power (read one)

  • 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.

Additional readings (based on lecture slides and lecture)

  • 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.

Lecture 3: Data science

  1. What is data science?
  2. An overview of the methods
  3. What is "big data"

What is big data and what it means for social sciences (read one)

  • boyd, d., & Crawford, K. (2012). Critical Questions for Big Data. Information, Communication & Society, 15(5), 662–679.
  • Golder, S. A., & Macy, M. W. (2014). Digital Footprints: Opportunities and Challenges for Online Social Research. Annual Review of Sociology, 40(1), 129–152.

What are computational methods?

  • 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.

Additional readings

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

Lecture 4: Network analysis

  1. Reflecting actors and ties - what forms a network?
  2. From descriptive to computational network analysis
  3. 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)

Lecture 5: Simulation

  1. Few classical simulation models
  2. 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.

Lecture 6: Complex systems and Lecture 7: Interactive applications for research

  1. Thinking in complex systems way
  2. Modelling as activity
  3. Why to apply complexity and why not?
  4. What are interactive applications and how they benefit social sciences

Complex systems (read one - tarkista tekstit)

  • Schneider, M., & Somers, M. (2006). Organizations as complex adaptive systems: Implications of complexity theory for leadership research. The Leadership Quarterly, 17(4), 351-365.
  • Urry, John. The complexity turn. Theory, Culture & Society 22.5 (2005): 1-14.

Interactive applications (read both)

  • Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. science, 311(5762), 854-856.
  • Raento, M., Oulasvirta, A., & Eagle, N. (2009). Smartphones. An emerging tool for social scientists. Sociological Methods & Research, 37(3), 426–454.