Technische Universität Ilmenau

Current challenges in computational communication science - Modultafeln of TU Ilmenau

The module lists provide information on the degree programmes offered by the TU Ilmenau.

Please refer to the respective study and examination rules and regulations for the legally binding curricula (Annex Curriculum).

You can find all details on planned lectures and classes in the electronic university catalogue.

Information and guidance on the maintenance of module descriptions by the module officers are provided at Module maintenance.

Please send information on missing or incorrect module descriptions directly to modulkatalog@tu-ilmenau.de.

module properties Current challenges in computational communication science in degree program Master Medien- und Kommunikationswissenschaft/Media and Communication Science 2013
module number101885
examination number2500405
departmentDepartment of Economic Sciences and Media
ID of group 2559 (Kommunikationswissenschaft mit Schwerpunkt Computational Communication Science)
module leaderProf. Dr. Emese Domahidi
term summer term only
languageenglisch
credit points6
on-campus program (h)22
self-study (h)158
obligationelective module
examalternative examination performance
details of the certificate

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

 

alternative examination performance due to COVID-19 regulations incl. technical requirements
signup details for alternative examinationsDie Anmeldung zur alternativen semesterbegleitenden Abschlussleistung erfolgt über das Prüfungsverwaltungssystem (thoska) außerhalb des zentralen Prüfungsanmeldezeitraumes. Die früheste Anmeldung ist generell ca. 2-3 Wochen nach Semesterbeginn möglich. Der späteste Zeitpunkt für die An- oder Abmeldung von dieser konkreten Abschlussleistung ist festgelegt auf den (falls keine Angabe, erscheint dies in Kürze):
  • signup begins: 05.11.2021
  • signup ends: 12.11.2021
  • resignation not after: 12.11.2021
  • last modification of this information: 15.10.2021
maximum number of participants
previous knowledge and experience

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

 

learning outcome

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.  

content

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.

 

media of instruction and technical requirements for education and examination in case of online participation

Course will be held online exclusively.

moodle: https://moodle2.tu-ilmenau.de/enrol/index.php?id=3405

literature / references

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. 

evaluation of teaching

Pflichtevaluation:

Freiwillige Evaluation:

SS 2019 (Seminar)

Hospitationen: