Technische Universität Ilmenau

Current challenges in computational communication science - Modultafeln of TU Ilmenau

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

Students are expected to engage in the seminar (20%) to present their projects (30%) and to write a term paper at the end of the semester (50%).

 

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: 12.06.2020
  • signup ends: 12.06.2020
  • resignation not after: 12.06.2020
  • last modification of this information: 12.06.2020
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

Current challenges in digital communication research: Data Science in Social Media: Graph Analysis and Text Mining in Practice

Digital media are an inherent part of everyday life in contemporary societies. Massive engagement of individuals and organizations in these media leaves insightful digital trace data of their activities and can potentially reveal users’ attitudes, beliefs and behaviors.

The newly founded field of data science applies methods from computer science to investigate meaningful social science research questions based on digital data. However, challenges in this field, like ethical concerns, the handling of ”big” datasets, and the development of appropriate theoretical approaches and useful research questions can be best solved in interdisciplinary teams. In this specialization module, we aim to conduct data science research projects with students from communication and computer science. Together we will focus on different (open) data sets, which are suited to investigate various social phenomena. Possible sources to extract data are:

 

  1. Stackexchange forums: https://archive.org/download/stackexchange
  2. GDELT database: https://www.gdeltproject.org/
  3. Mediacloud: https://mediacloud.org
  4. Twitter
  5. Propublica free datasets related to social media: https://www.propublica.org/datastore/datasets/politics
  6. Data.world datasets related to social media: https://data.world/

We will develop relevant research questions, appropriate data analysis and visualization strategies, and discuss the implications of the research projects’ findings. Students will learn data science using Python and R and will have an opportunity to work in interdisciplinary teams. For communication science students no former knowledge of R or Python is required, however, the willingness to get acquainted with these methods is mandatory. 

media of instruction
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)

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