Computational Social Science[go to overview]
- The purpose of the module is to acquaint students with the essential approaches and methods in the Computational Social Science field.
- This includes knowledge about potentials and limitations of nonreactive survey research procedures for the social sciences, in particular methods of social networks analysis.
- The students will be trained to plan, carry out and evaluate on their own empirically-based studies on the basis of new data forms (e.g. from social media).
- The seminar introduces students into the research area of Computational Social Science.
- The students will be trained to select, test, apply and provisionally evaluate methods from the computer science field in order to answer social science questions.
- A basic understanding of the possible application of Data Mining methods will be developed for this purpose, as well as an understanding of the significance and possibilities of operationalization of issues and problems established in the social sciences.
- The content of the module include at least following areas : Social Science Research Questions and Methods, The Small World Phenomenon, Search in Social Networks, Python and Octave tutorials, Social Network Analysis, Affiliation Networks, Community Detection, Social network models and Generation.
- written exam: 80% and coding assignments: 20%
- paper and pen exercises: For taking part in the exam, solutions for all but one paper and pen exercise have to be submitted
- home assignment (code submissions)
Due to WWW conference 2016, the lecture will start one week later, on Wednesday 20th of April.
- Stanley Wasserman and Katherine Faust, Social Network Analysis - Methods and Applications, 1995
- Monge, Peter R., and Noshir S. Contractor. Theories of communication networks. New York: Oxford University Press, 2003.
- David Easley and Jon Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World, 2010 (free online book)