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.
All actitivity reports must be handed in by 11.1. at 14:45.
7.1. 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
7.1. 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
8.1. Theme 3: Studying web sites
- Content 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).
8.1. Theme 4: Studying algorithms and source code
- Kitchin, R. (2017). Thinking critically about and researching algorithms. Information, Communication & Society, 20(1), 14–29.
9.11. 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
9.1. Theme 6: (Introduction to) machine learning for social sciences
10.1. 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.
10.1. 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).
11.1. 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.
11.1. Summary and review