Introduction to Computational Communication Science - Interactive curriculae of TU Ilmenau
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| module properties Introduction to Computational Communication Science in degree program Master Media and Communication Science 2021 | |
|---|---|
| module number | 200833 |
| examination number | 2500592 |
| department | Department of Economic Sciences and Media |
| ID of group | 2559 (Kommunikationswissenschaft mit Schwerpunkt Computational Communication Science) |
| module leader | Prof. Dr. Emese Domahidi |
| term | winter term only |
| language | Englisch |
| credit points | 5 |
| on-campus program (h) | 22 |
| self-study (h) | 128 |
| obligation | elective module |
| exam | oral examination performance, 30 minutes |
| details of the certificate | 15 minutes preparation time, followed by 30 minutes oral examination |
| link to Moodle course | https://moodle.tu-ilmenau.de/course/view.php?id=2531 |
| teacher | |
| signup details for alternative examinations | <p style="-qt-block-indent: 0; text-indent: 0px; margin: 0px;">Enrolment in the course takes place by registering in the Moodle room by 20 October 2025. Registration for the exam is done directly by the Examination Office based on the list of participants.</p><p> </p> |
| maximum number of participants | 30 |
| previous knowledge and experience | Familiarity with empirical methods and quantitative data analysis in communication research Data analysis software skills (e.g. R, Python) are a plus, however, not mandatory. |
| learning outcome | Students can review relevant literature on specific topics in computational communication science. Students can understand the theoretical background behind the field of computational social and communication science. Students are familiar with different computational methods (e.g. sentiment analysis, supervised machine learning) during the course. On the basis of recommended readings participants can apply and evaluate these theoretical perspectives and methods. |
| content | 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 digital data. Important topics, such as ethics and availability of digital data, will be reviewed in the seminar. Students will also 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.
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| media of instruction and technical requirements for education and examination in case of online participation | This course is only in presence.
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| literature / references | Will be announced each semester. |
| evaluation of teaching | |