"Limited reading can, and often does, induce a risky sense of competence. I sustain that it is our scholarly responsibility to understand the methodology well. Such understanding gives us better access to a powerful research methodology that offers much freedom to operate within its framework in a creative manner." (Walsh et al., 2015)
|Date||Lecture focus||Assignments (for this lecture)|
|18.3.||Introduction to course practices|
Working with files
Working with natural language
|25.3.||Easter holiday - no class|
|1.4.||Working with application programming interfaces (APIs)|
Working with web scraping
McKelvey, K., DiGrazia, J., & Rojas, F. (2014). Twitter publics: how online political communities signaled electoral outcomes in the 2010 US house election. Information, Communication & Society, 17(4), 436–450.
Jungherr, A., Schoen, H., & Jürgens, P. (2016). The Mediation of Politics through Twitter: An Analysis of Messages posted during the Campaign for the German Federal Election 2013. Journal of Computer-Mediated Communication, 21(1), 50–68.
Note: class ends stats at 8.15 and ends 10.15 sharp!
|Summary of machine learning methods in this class|
Lab: Data management and preprocessing
Lab: Initially formulating research questions
Homework: Think of a research theme that you're interested and prepeare to share it with the class.
|15.4||Classifying: support vector machines|
Classifying: decision trees (optional)
|Homework: Return core related work to Matti by Friday 15.4. noon.|
Nelimarkka & Ahonen: Automatically detecting deliberation
Hanak et al.: Tweetin` in the Rain.
Support vector machines, focus on understanding the linear version only; the non-linear variants are "extensions of the same idea"
Decision trees, read 3.1 - 3.3 and skim 3.4
|22.4||Finding groups: k-means (optional)|
Searching patterns: Association rules
|Nelimarkka et al. Social learning strategies|
k-means (read algorithm and discussions)
Association rules (read definition, concepts and process)
|29.4.||Searching patterns: Bayes networks (optional)|
Finding groups: topic models
Lab: Framing an exact research question
|Homework: Return an initial research plan of (max 2 pages, use line space 2) by Tuesday 26.4. noon to Matti.|
Nokelainen & Tirri: Role of motivation in the moral and religious judgment of mathematically gifted adolescents
Nelimarkka et al. Agenda normalisation work
Topic models (you can also check the page on LDA, if you understand the model that's OK already)
Bayesian network (read the example)
|6.5.||Summary: validity and reliability challenges|
Lab: Choosing an analysis method and planning analysis strategy
|Laaksonen et al. Big data augmented ethnography. *|
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.
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.
Please contact Matti Nelimarkka (firstname.lastname@example.org).
For personal tutoring, see the online calendar and choose a time and date.