The course is held on the first (I) study period of 2015-2016. You can sign up for the course at weboodi. The lectures follow the schedule given below:

PROGRAM
LECTURE 1: Tuesday, 01.09. 16:00 – 18:00, U35, Seminar room 114

LECTURE 2: Wednesday, 02.09. 16:00 – 18:00, U35, Seminar room 113

LECTURE 3: Monday 07.09.  16:00 – 18:00, U35, Seminar room 114

LECTURE 4: Monday 14.9. 16:00 – 18:00, U35, Seminar room 114

LECTURE 5: Tuesday 15.09. 16:00 – 18:00, U35, Seminar room 114

LECTURE 6: Wednesday 16.09.15 16:00 – 18:00, U35, Seminar room 113

LECTURE 7: Monday 21.09. 16:00 – 18:00, U35, Seminar room 114

LECTURE 8: Tuesday 22.09. 17:00 – 19:00, U35, Seminar room 114

The lectures are at Unioninkatu 35, seminar room 114, with two exceptions: on 2.9 and 16.9 the lecture is at Unioninkatu 35, seminar room 113.

All lecture slides will be provided online via this web page.

 

 

COURSE INFORMATION:

Introduction to Computational Social Science, 5op
University of Helsinki

Target audience
This course is targeted at master’s level students, doctoral students and researchers who are interested in applying computational and digital methods in the domain of social sciences. The course is given in English. No previous experience in computational or digital methods is required.

 

Objective
The aim of the introductory course is to give an overview of the different domains of computational social science; (1) big data and society, (2) social networks, (3) social complexity and (4) simulation. After the course one should have an understanding of the different aspects of computational social science, its methods and how to approach them in one’s own research.

Contents
Digitalisation is rapidly changing the ways social science is being researched. The change is no longer about how we research the digital realm, but more and more about the new viewpoints we are able to get on the society as a whole. Social Science is on the verge of a paradigm shift that allows us to ask and answer questions that were unthinkable a few decades a go.

That being said, using computational methods is nothing new in social sciences. These methods and tools have been around for decades. The paradigm shift does not come from these computational methods as they are, but from their simultaneous convergence with (1) the exponential developments in IT, (2) drastically lower costs of processing power and data storages, (3) better availability and access to vast sets of data and tools (4) and the way major areas of our daily lives and our societies have been digitalised.

The course is formed around four major topics that all fall under the emerging field of computational social science:

1. Big data and society
2. Social Networks
3. Social Complexity
4. Simulation

Literature
Cioffi-Revilla, Claudio (2014). Introduction to Computational Social Science. Springer-Verlag, London.

Completion
Participation and a short research plan that applies computational methods to a research problem.

Evaluation
The grading scale is from 0 to 5:

1,2,3,4,5
Fail (F), 1 (Sufficient = E), 2 (Satisfactory = D), 3 ( Good = C), 4 (Very Good = B ), 5 (Excellent = A).

Responsible person               

Lauri Eloranta, M.Sc. (Computer Science), M.Soc.Sc. (Communication), PhD candidate.

 

COURSE BOOK

Cioffi-Revilla, Claudio (2014). Introduction to Computational Social Science. Springer-Verlag, London.

The course book is available as an e-book via Helsinki University Library: https://helka.linneanet.fi/cgi-bin/Pwebrecon.cgi?BBID=2753081

 

 

ADDITIONAL READING FOR THE COURSE

LECTURE 1 – Introduction to Computational Social Science

Lazer, D. et al. 2009. Computational Social Science. Science. 6 February 2009: Vol.
323, no. 5915, pp. 721-723.

Conte, R. 2012. Manifesto of Computational Social Science. The European
Physical Journal Special Topics. November 2012: Vol. 214, Issue 1, pp. 325-346.

Anderson, C. 2008. The End of Theory: The Data Deluge Makes the Scientific
Method Obsolete. Wired.
http://archive.wired.com/science/discoveries/magazine/16-07/pb_theory

Einav, L. and Levin, J. 2014. The Data Revolution and Economic Analysis. in
Innovation Policy and the Economy edited by Josh Lerner and Scott Stern.
http://web.stanford.edu/~leinav/pubs/IPE2014.pdf

King, G. 2011. Ensuring the Data-Rich Future of the Social Sciences. Science. 11
February 2011: Vol. 331 no. 6018 pp. 719-721.

Wallach, H. 2014. Big Data, Machine Learning, and the Social Sciences: Fairness,
Accountability, and Transparency. Medium.com.
https://medium.com/@hannawallach/big-data-machine-learning-and-thesocial-sciences-927a8e20460d

LECTURE 2 – Bascis of Computation and Modeling

Gentzkow, M.; Shapiro, J, M. 2014. Code and Data for the Social Sciences: A Practitioner’s Guide. University of Chicago mimeo, http://faculty.chicagobooth.edu/matthew.gentzkow/research/CodeAndData.pdf

Granger, C. 2015. Coding is not the new literacy. http://www.chris-granger.com/2015/01/26/coding-is-not-the-new-literacy/

Epstein, J. M. 2008. Why Model?. Keynote address to the Second World Congress on Social Simulation. In Why Model?: Keynote address to the Second World Congress on Social Simulation. George Mason University.

Page, S. E. 2012. The Model Thinker: Prologue, Introduction and Chapter 1. Link provided by University of Michigan & Coursera: http://vserver1.cscs.lsa.umich.edu/~spage/ONLINECOURSE/R1Page.pdf

Stanford Encyclopedia of Philosophy, 2012. Models in Science. http://plato.stanford.edu/entries/models-science/

Bell, D. 2003. UML basics: An introduction to the Unified Modeling Language. The Rational Edge. https://www.ibm.com/developerworks/rational/library/content/RationalEdge/sep03/f_umlbasics_db.pdf

LECTURE 3 – Big Data & Data Mining

Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37.

De Mauro, A., Greco, M., Grimaldi, M. 2014. What is big data? A consensual definition and a review of key research topics. 4th International Conference on Integrated Information, Madrid

LECTURE 4 – Social Network Analysis

Borgatti, S. P.; Mehra, A.; Brass, D. J.; Labianca, G. (2009). Network Analysis in the Social Sciences. Science 13 February 2009: 323 (5916), 892-895.

de Sola Pool, I., & Kochen, M. (1979). Contacts and influence. Social networks, 1(1), 5-51.

Tantipathananandh, C., Berger-Wolf, T., & Kempe, D. (2007). A framework for community identification in dynamic social networks. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 717-726). ACM.

Kossinets, G., & Watts, D. J. (2006). Empirical analysis of an evolving social network. Science, 311(5757), 88-90.

Fowler, J. H., & Christakis, N. A. (2008). Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study. Bmj, 337, a2338.

Tichy, N. M., Tushman, M. L., & Fombrun, C. (1979). Social network analysis for organizations. Academy of management review, 4(4), 507-519.

Kempe, D., Kleinberg, J., & Tardos, É. (2003, August). Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 137-146). ACM.

LECTURE 5 – Complex Social Systems

Miller, J. H., & Page, S. E. (2004). The standing ovation problem. Complexity, 9(5), 8-16.

Lansing, J. S. (2003). Complex adaptive systems. Annual review of anthropology, 183-204.

Geli-Mann, M. (1994). Complex adaptive systems. Complexity: Metaphors, models and reality, 17-45.

Innes, J. E., & Booher, D. E. (1999). Consensus building and complex adaptive systems: A framework for evaluating collaborative planning. Journal of the American Planning Association, 65(4), 412-423.

Holland, J. H. (2006). Studying complex adaptive systems. Journal of Systems Science and Complexity, 19(1), 1-8.

Levin, S. (2003). Complex adaptive systems: exploring the known, the unknown and the unknowable. Bulletin of the American Mathematical Society, 40(1), 3-19.

Tan, J., Wen, H. J., & Awad, N. (2005). Health care and services delivery systems as complex adaptive systems. Communications of the ACM, 48(5), 36-44.

LECTURE 6 – Simulation in Social Sciences

Schelling, T. C. (1971). Dynamic models of segregation. Journal of mathematical sociology, 1(2), 143-186.

Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(suppl 3), 7280-7287.

Railsback, S. F., Lytinen, S. L., & Jackson, S. K. (2006). Agent-based simulation platforms: Review and development recommendations. Simulation, 82(9), 609-623.

Forrester, J. W. (1994). System dynamics, systems thinking, and soft OR. System Dynamics Review, 10(2‐3), 245-256.

Forrester, J. W. (1993). System dynamics and the lessons of 35 years. In A systems-based approach to policymaking (pp. 199-240). Springer US.

Wolfram, S. (1984). Universality and complexity in cellular automata. Physica D: Nonlinear Phenomena, 10(1), 1-35.

LECTURE 7 – Ethical and Legal Issues in Computational Social Science

Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society, 15(5), 662-679.

Zwitter, A. (2014). Big Data ethics. Big Data & Society, 1(2), 2053951714559253.

Richards, N. M., & King, J. H. (2014). Big data ethics. Wake Forest Law Review.

Neuhaus, F., & Webmoor, T. (2012). Agile ethics for massified research and visualization. Information, Communication & Society, 15(1), 43-65.

Lyon, D. (2014). Surveillance, snowden, and big data: capacities, consequences, critique. Big Data & Society, 1(2), 2053951714541861.

LECTURE 8 – A Summary of Computational Social Science

Wallach, H. (2015). Computational social science: Toward a collaborative future. In R. Alvarez, editor, Computational Social Science: Discovery and Prediction. Cambridge University Press, forthcoming.

Watts, D. J. (2013). Computational social science: Exciting progress and future directions. The Bridge on Frontiers of Engineering, 43(4), 5-10.