Current challenges in computational communication science - Interactive curriculae of TU Ilmenau

R knowledge is not required, however appreciated. Basic introduction to programming will be provided.

Students are recommended to attend the research module „Computational Communication Research” (Prof. Domahidi) in parallel to the seminar in order to get hands-on experience of working with digital data and R. Learn and practice automated text analysis techniques.  

Today, it is difficult to imagine our lives without Wikipedia, Google, Facebook, Instagram, iPhones, Wi-Fi, YouTube, Twitter, and other advances of the digital era. Spending most of our lives online, we leave digital footprints of our daily interactions and activities. Yes, all the pictures of kittens you liked last year on Facebook are now a part of traceable digital data available for social science research. We now have a unique opportunity to collect enormous amount of data on social behavior of human beings. However, the volume and heterogeneity of "big data" constitutes a challenging task for social scientists often discouraging them from analyzing precious material.

This course will focus mostly on social and communication science providing at the same time the very basic understanding of new computational methods that can be employed to collect and process ”big data”. Important topics, such as ethics and availability of digital data, will be reviewed in the seminar. Students will get a glimpse at the methods of automated text analysis, which has become an essential skill for every communication specialist. Knowledge received in the class can be further applied in the field of journalism, marketing, and advertising.

This course provides the necessary background to the theory of computational communications science. Students are recommended to take the research modules „Computational Communication Research” in parallel to the seminar in order to get hands-on experience of working with digital data and R.

The course will be taught on-site/ hybrid. The class cannot be studied entirely digital – it is therefore not suitable for students enrolled in the digital MCS-program.

moodle: Kurs: Data Science in Social Media: Graph Analysis and Text Mining in Practice (tu-ilmenau.de)

The literature mentioned here is a good starting point to get acquainted with the topics. However, this list is not complete. You are expected to conduct your own literature. Research.

Data Science in R

Arnold, J.B. (2018). R for Data Science Solutions. https://jrnold.github.io/r4ds-exercise-solutions/

Silge, J. & Robinson, D. (2017). Text Mining with R: A Tidy Approach (1st ed.). O'Reilly Media, Inc.. https://www.tidytextmining.com/

Wickham, H. & Grolemund, G. (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (1st ed.). O'Reilly Media, Inc.. http://r4ds.had.co.nz/

Papers on the Stackexchange dataset

Anderson, A., Huttenlocher, D., Kleinberg, J., & Leskovec, J. (2012). Discovering value from community activity on focused question answering sites: a case study of stack overflow. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (S. 850–858). ACM.

Marder, A. (2015). Stack Overflow Badges and User Behavior: An Econometric Approach. In Proceedings of the 12th Working Conference on Mining Software Repositories (S. 450–453). Piscataway, NJ, USA: IEEE Press. Abgerufen von http://dl.acm.org/citation.cfm?id=2820518.2820584

Posnett, D., Warburg, E., Devanbu, P., & Filkov, V. (2012). Mining Stack Exchange: Expertise Is Evident from Initial Contributions. In 2012 International Conference on Social Informatics (S. 199–204). https://doi.org/10.1109/SocialInformatics.2012.67

Vasilescu, B., Serebrenik, A., Devanbu, P., & Filkov, V. (2014). How social Q&A sites are changing knowledge sharing in open source software communities. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing (S. 342–354). ACM. http://dl.acm.org/citation.cfm?id=2531659

Papers on the GDELT dataset

Kwak, H. & An, J. (2014). A First Look at Global News Coverage of Disasters by Using the

GDELT Dataset . In. Luca Maria Aiello, L.M., & McFarland, D. (Eds.) Proceedings of Social Informatics 6th International Conference, SocInfo 2014, Barcelona, Spain, November 11-13, 2014

Vargo, C. J., & Guo, L. (2017). Networks, big data, and intermedia agenda setting: An analysis of traditional, partisan, and emerging online US news. Journalism & Mass Communication Quarterly, 94(4), 1031–1055.

Vargo, C. J., & Guo, L. (2017). Networks, big data, and intermedia agenda setting: An analysis of traditional, partisan, and emerging online US news. Journalism & Mass Communication Quarterly, 94(4), 1031–1055.

Vargo, C. J., Guo, L., & Amazeen, M. A. (2018). The agenda-setting power of fake news: A big data analysis of the online media landscape from 2014 to 2016. new media & society, 20(5), 2028–2049. 

The grades will be based on the evaluation of a short term paper.

Dieses Modul enthält mindestens eine alternative semesterbegleitende Abschlussleistung. Bitte beachten Sie, dass diese in der Regel schon zu Beginn des Semesters, in dem diese angeboten wird, angemeldet werden muss.
Über die Details und Zeiträume dazu werden Sie vom Lehrenden und/oder dem Prüfungsamt informiert. Fragen Sie gegebenenfalls unbedingt beim Lehrenden nach.

This module contains at least one alternative exam part. Please note that this must usually be registered at the beginning of the semester in which it is offered.
The lecturer and/or the examination office will inform you about the details and time periods. If necessary, be sure to ask the lecturer.

The grades will be based on the evaluation of a short term paper.

Dieses Modul enthält mindestens eine alternative semesterbegleitende Abschlussleistung. Bitte beachten Sie, dass diese in der Regel schon zu Beginn des Semesters, in dem diese angeboten wird, angemeldet werden muss.
Über die Details und Zeiträume dazu werden Sie vom Lehrenden und/oder dem Prüfungsamt informiert. Fragen Sie gegebenenfalls unbedingt beim Lehrenden nach.

This module contains at least one alternative exam part. Please note that this must usually be registered at the beginning of the semester in which it is offered.
The lecturer and/or the examination office will inform you about the details and time periods. If necessary, be sure to ask the lecturer.