Fall 2019

The aim

The focus this year is on the methods to study digital society. This includes examining digital objects (such as websites, social media, digital products) and examining the society through digital methods The scope in this study period includes qualitative, quantitative and computational approaches. The content focuses on

  • Studying digital objects

    • Studying websites
    • Studying social media services
    • Studying algorithms and source code
    • Studying digital products
  • Studying with digital methods

    • (Introduction to) machine learning for social sciences
    • Networks and their analysis
    • Crowdsourcing in social sciences
    • Building digital research tools


The course is evaluated through several assigments we'll work during the classes. Kindly report your learnings and answers to these materials continiously through the class.

Return all assigment drafts on 12.12. at 12:00 via this link in pdf-format. Do not include your student ID, but include your name.

All actitivity reports must be handed in by 10.1. at 12:00. Compile all your assigments into a single PDF and return it via this link.


21.11. Theme 1: Introduction and course structure

Read before class

  • King, G. (2014). Restructuring the Social Sciences: Reflections from Harvard’s Institute for Quantitative Social Science. PS: Political Science & Politics, 47(1), 165–172. http://doi.org/10.1017/S1049096513001534
  • Kuehn, D., & Rohlfing, I. (2016). Are there really two cultures? A pilot study on the application of qualitative and quantitative methods in political science. European Journal of Political Research, 55(4), 885–905. http://doi.org/10.1111/1475-6765.12159

21.11. Theme 2: Studying social media services

  • 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. http://doi.org/10.1111/jcc4.12143
  • Hargittai, E. (2015). Is Bigger Always Better? Potential Biases of Big Data Derived from Social Network Sites. The ANNALS of the American Academy of Political and Social Science, 659(1), 63–76. http://doi.org/10.1177/0002716215570866

26.11. Theme 3: Studying web sites

  • Content analysis

  • Link analysis

  • Gibson, R., & Ward, S. (2000). A Proposed Methodology for Studying the Function and Effectiveness of Party and Candidate Web Sites. Social Science Computer Review, 18(3), 301–319.

  • Brugger, N. (2013). Historical Network Analysis of the Web. Social Science Computer Review, 31(3).

26.11. Theme 4: Studying algorithms and source code

  • Kitchin, R. (2017). Thinking critically about and researching algorithms. Information, Communication & Society, 20(1), 14–29.

28.11. Theme 5: Studying digital products

  • Silfverberg, S., Liikkanen, L. A., & Lampinen, A. (2011, March). I'll press play, but I won't listen: profile work in a music-focused social network service. In Proceedings of the ACM 2011 conference on Computer supported cooperative work (pp. 207-216). ACM.
  • Light, B., Burgess, J., & Duguay, S. (2016). The walkthrough method: An approach to the study of apps. New Media & Society, 146144481667543. http://doi.org/10.1177/1461444816675438

28.11. Theme 6: (Introduction to) machine learning for social sciences

  • Automated tools for text classification

  • Analyzing images and videos in scale

  • Quantitative methods and machine learning approaches

  • 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.

3.12. Theme 7: Networks and their analysis

  • Larsson, A. O. (2013). Tweeting the Viewer -- Use of Twitter in a Talk Show Context. Journal of Broadcasting & Electronic Media, 57(2), 135–152.
  • Ogan, C., & Varol, O. (2016). What is gained and what is left to be done when content analysis is added to network analysis in the study of a social movement: Twitter use during Gezi Park. Information, Communication & Society, 4462 (June), 1–19.

3.12. Theme 8: Crowdsourcing in social sciences

  • Crump, M. J. C., McDonnell, J. V., & Gureckis, T. M. (2013). Evaluating Amazon’s Mechanical Turk as a Tool for Experimental Behavioral Research. PLoS ONE, 8(3).

12.12. Theme 9: Building digital research tools

  • Fourney, A., & Morris, M. (2013). Enhancing Technical Q&A Forums with CiteHistory. In Proceedings of ICWSM 2013.
  • 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.

12.12. Summary and review

Class materials