Institute for Web Science and Technologies · Universität Koblenz - Landau
Institute WeST
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Computational Social Science

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Summer Terms 2019

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.

Measurements and Data Biases

Intro to Text Analysis
1.5. Holiday  
8.5. Student Presentations - TOPICS Student Presentations
15.5. Intro to Network Analysis Team Formation
22.5. Algorithmic Auditing T: Dependency management, git, binder
29.5. Socio-linguistics Team consults
5.6.

Analytic Sociology, literature

 T: scikit learn; Team consults
12.6. Holiday Holiday
19.6. Correlations, Regressions and Causality Team consults: Status report all teams
26.6. Team consults Team consults
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.

Lecturers

JProf. Dr. Claudia Wagner
  • clwagner@uni-koblenz.de
  • Professor
  • B 006