Technische Universität Ilmenau

Applied Social Network Analysis for Communication Research - Modultafeln of TU Ilmenau

The Modultafeln have a pure informational character. The legally binding information can be found in the corresponding Studienplan and Modulhandbuch, which are served on the pages of the course offers. Please also pay attention to this legal advice (german only). Information on place and time of the actual lectures is served in the Vorlesungsverzeichnis.

subject properties Applied Social Network Analysis for Communication Research in major Master Medien- und Kommunikationswissenschaft/Media and Communication Science 2013
subject number101937
examination number2500431
departmentDepartment of Economic Sciences and Media
ID of group 2559 (Kommunikationswissenschaft mit Schwerpunkt Computational Communication Science)
subject leader Mian Waqas Ejaz
term ganzjährig
credit points6
on-campus program (h)22
self-study (h)158
Obligationobligatory elective
examalternative examination performance
details of the certificate

Frequent and active participation during class and research presentation – Preparing a Term Paper

maximum number of participants30
previous knowledge and experience

The course requires basic knowledge or affinity of field of social media communication, network analysis and R

learning outcome

Students are expected to learn the following things during the course:

  • Theoretical understanding of Network Analysis
  • Understand basic data structures required for Network analysis
  • Automated Web data scrapping particularly social media data
  • Data munging and transforming
  • Visualizing networks

The course is structured to make students aware usability of social media data for network analysis. Moreover, the course is primarily focused on communicating climate change issue and how the debate around this topic exists on social media. In the course, students are expected to learn different data structures and will be trained to use Twitter data. The instructor will make sure students get comfortable in using programming language R for data scrapping and analyzing. Finally, students will develop an understanding to visualize and explain different networks.

media of instruction
literature / references
  • Stinerock, R. (2018): Statistics with R – A Begniner’s Guide. New Delhi: Sage
  • Field, A. (2012): Discovering Statistics Using R. London, Thousand Oaks and New Delhi: Sage.
  • Robins, G. (2015): Doing Social Network Research: Network-based Research Design for Social Scientists. London: Sage
  • Häussler, T. (2017): Heating up the debate? Measuring fragmentation and polarisation in a German climate change hyperlink network. Social Network.
evaluation of teaching