Sie sind hier

Computational Social Science

https://klips.uni-koblenz-landau.de/v/112823

Lecture: Wednesday 14:15-15:45 (K 101)  - first class: 10.04.2019
Exercise: Wednesday 16:00-17:30 (E 313)  - first class: 10.04.2019

 

  LECTURE (14:15)- Now in room K 101 EXERCISE (16:00)
10.4. CSS Introduction T: Python tutorial, Pandas tutorial 
17.4. Scientific Data Analysis Measurements and Data Biases
24.4. Intro to Text Analysis Intro to Network Analysis
1.5. Holiday  
8.5. Student Presentations Student Presentations
15.5. Analytic Sociology Team Formation
22.5. Algorithmic Auditing T: Git & python & binder
29.5. Socio-linguistics T: statsmodel, scikit learn
5.6. Network Measures Team consults
12.6. Holiday Holiday
19.6. Correlations, Regressions Team consults: Status report all teams
26.6. Dynamics in Networks  
3.7. Inequality Theory and Measurements  
10.7. Final Presentations Final Presentations
17.7.    
24.7. Submission of Final Report  

Books:

  • ·        Salganik, M. J. (2017). Bit by bit: Social research in the digital age. Princeton, NJ: Princeton University Press.
  • ·        Stanley Wasserman and Katherine Faust, Social Network Analysis - Methods and Applications, 1995
  • ·        Dive into Python: https://www.diveinto.org/python3/

Papers:

  • ·        Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutman, M., Jebara, T., King, G., & Alstyne, M. V. (2009). Computational social science. Science, 323(5915), 721–723. doi:10.1126/science.1167742.
  • ·        Strohmaier, M. & Wagner, C. (2014). Computational social science for the world wide web. IEEE Intelligent Systems, 29(5), 84–88. doi:10.1109/MIS.2014.80.
  • ·        Golder, S. A. & Macy, M. W. (2014). Digital footprints: Opportunities and challenges for online social research. Annual Review of Sociology, 40, 129–152. doi:10.1146/annurevsoc-071913-043145.
  • ·        Jungherr, A. (2018). Normalizing digital trace data. In N. J. Stroud & S. C. McGregor (Eds.), Digital discussions: How big data informs political communication. New York, NY: Routledge.
  • ·        Howison, J., Wiggins, A., & Crowston, K. (2011). Validity issues in the use of social network analysis with digital trace data. Journal of the Association for Information Systems, 12(12), 767–797.
  • ·        Mayer-Schönberger, V. & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. New York, NY: Houghton Mifflin.
  • ·        Puschmann, C. & Burgess, J. (2013). The politics of Twitter data. In K. Weller, A. Bruns, J. Burgess, M. Mahrt, & C. Puschmann (Eds.), Twitter and Society (pp. 43–54). New York, NY: Peter Lang Publishing.
  • ·        Rogers, R. (2013a). Debanalizing Twitter: The transformation of an object of study. In H. Davis, H. Halpin, A. Pentland, M. Bernstein, & L. Adamic (Eds.), Websci 2013: Proceedings of the 5th annual acm web science conference (pp. 356–365). New York, NY: ACM. doi:10.1145/2464464.2464511.

JProf. Dr. Claudia Wagner

clwagner@uni-koblenz.de