The course provides an overview of potential computational approaces and methods to address social science research questions.
We will achieve this through discussion of key concepts and literature on this topic.
Pre-readings are my approach to engage you to the 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
- Before each lecture where you point out what you find interesting in the pre-readings, what you find problematic and reflect the key points in the articles.
- 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 is motivated by the academy of Finland application forms for early career researchers.
You must submit a [Postdoctoral Researcher applications](http://www.aka.fi/en/funding/how-to-apply/application-guidelines/research-plan-guidelines/] and reguired appendixes.
Follow the Academy of Finland guidelines on content, length etc.
Naturally, you won't be using the Academy of Finland online form, but rather just a single document.
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: Introduction
Goal of lecture
- Course practices and organization
- What is computational social science?
Readings: Pick one:
- 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
Reading: everyone reads
- Coding social science: Introduction (manuscript, available in Yammer)
Lecture 2: Data science
- What is data science?
- An overview of the methods
- What is "big data"
Readings: pick 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.
- 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.
Lecture 3: Network analysis
- Reflecting actors and ties - what forms a network?
- From descriptive to computational network analysis
- Key jargon
- Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science, 323(5916), 892-895.
Lecture 4: Simulation & Complex systems
- Few classical simulation models
- How to choose parameters?
- Thinking in complex systems way
- Modelling as activity
- Why to apply complexity and why not?
- Skim Chapters 1 and 2 from Gilbert G. Nigel, Troitzsch Klaus G. (2005). Simulation for the social scientist. Open University Press.
- Urry, John. The complexity turn. Theory, Culture & Society 22.5 (2005): 1-14.
Lecture 5: Interactive applications for research
- What are interactive applications and how they benefit social sciences
- Draft in Yammer
- Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science (New York, N.Y.), 311(5762), 854–856. https://doi.org/10.1126/science.1121066
Lecture 6: Validity and ethics
- 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.
- Bucher, T. (2016). Machines dont have instincts: Articulating the computational in journalism. New Media & Society.
- 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.
Lecture 7: Integrating computational methods into a research project
- Laaksonen, S.-M. M., Nelimarkka, M., Tuokko, M., Marttila, M., Kekkonen, A., & Villi, M. (2017). Working the fields of big data: Using big-data-augmented online ethnography to study candidate–candidate interaction at election time. Journal of Information Technology and Politics, 14(1), 110–131. https://doi.org/10.1080/19331681.2016.1266981
- Muller, M., Guha, S., Baumer, E. P. S., Mimno, D., & Shami, N. S. (2016). Machine Learning and Grounded Theory Method. In Proceedings of the 19th International Conference on Supporting Group Work - GROUP ’16 (pp. 3–8). New York, New York, USA: ACM Press. https://doi.org/10.1145/2957276.2957280
- Nelson, L. K. (2017). Computational Grounded Theory. Sociological Methods & Research, 004912411772970. https://doi.org/10.1177/0049124117729703
- Bartlett, A., Lewis, J., Reyes-Galindo, L., & Stephens, N. (2018). The locus of legitimate interpretation in Big Data sciences: Lessons for computational social science from -omic biology and high-energy physics. Big Data & Society, 5(1), 205395171876883. https://doi.org/10.1177/2053951718768831
- King, G. (2014). Restructuring the Social Sciences: Reflections from Harvard’s Institute for Quantitative Social Science. PS: Political Science & Politics, 47(01), 165–172. https://doi.org/10.1017/S1049096513001534
- Saltz, J., Shamshurin, I., & Connors, C. (2017). Predicting data science sociotechnical execution challenges by categorizing data science projects. Journal of the Association for Information Science and Technology, 68(12), 2720–2728. https://doi.org/10.1002/asi.23873
Non-mandatory commenting and peer-commentng session
29.10. 9.00 (sharp) - 11ish
Location: Unioninkatu 35, meeting room 344
Send your proposal drafts via email by Friday 26th to Matti.
Final deadline for research proposal
Friday 14th, December, via email to matti