Technische Universität Ilmenau

Current challenges in computational communication science - Modultafeln der TU Ilmenau

Die Modultafeln sind ein Informationsangebot zu den Studiengängen der TU Ilmenau.

Die rechtsverbindlichen Studienpläne entnehmen Sie bitte den jeweiligen Studien- und Prüfungsordnungen (Anlage Studienplan).

Alle Angaben zu geplanten Lehrveranstaltungen finden Sie im elektronischen Vorlesungsverzeichnis.

Informationen und Handreichungen zur Pflege von Modulbeschreibungen durch die Modulverantwortlichen finden Sie unter Modulpflege.

Hinweise zu fehlenden oder fehlerhaften Modulbeschreibungen senden Sie bitte direkt an modulkatalog@tu-ilmenau.de.

Modulinformationen zu Current challenges in computational communication science im Studiengang Master Medien- und Kommunikationswissenschaft/Media and Communication Science 2013
Modulnummer101885
Prüfungsnummer2500405
FakultätFakultät für Wirtschaftswissenschaften und Medien
Fachgebietsnummer 2559 (Kommunikationswissenschaft mit Schwerpunkt Computational Communication Science)
Modulverantwortliche(r)Prof. Dr. Emese Domahidi
TurnusSommersemester
Spracheenglisch
Leistungspunkte6
Präsenzstudium (h)22
Selbststudium (h)158
VerpflichtungWahlmodul
Abschlussalternative Prüfungsleistung
Details zum Abschluss

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

 

Alternative Abschlussform aufgrund verordneter Corona-Maßnahmen inkl. technischer Voraussetzungen
Anmeldemodalitäten für alternative PL oder SLDie 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):
  • Anmeldebeginn: 05.11.2021
  • Anmeldeschluss: 12.11.2021
  • Rücktrittsfrist: 12.11.2021
  • letzte Änderung der Fristen: 15.10.2021
max. Teilnehmerzahl
Vorkenntnisse

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

 

Lernergebnisse und erworbene Kompetenzen

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.  

Inhalt

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.

 

Medienformen und technische Anforderungen bei Lehr- und Abschlussleistungen in elektronischer Form

Course will be held online exclusively.

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

Literatur

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. 

Lehrevaluation

Pflichtevaluation:

Freiwillige Evaluation:

SS 2019 (Seminar)

Hospitationen: