Computational Communication Research - Modultafeln of TU Ilmenau
The Modultafeln have a pure informational character. The legally binding information can be found in the corresponding Studienplan and Modulhandbuch, which are served on the pages of the course offers. Please also pay attention to this legal advice (german only). Information on place and time of the actual lectures is served in the Vorlesungsverzeichnis.
|subject properties Computational Communication Research in major Master Medien- und Kommunikationswissenschaft/Media and Communication Science 2013|
|department||Department of Economic Sciences and Media|
|ID of group||2559 (Kommunikationswissenschaft mit Schwerpunkt Computational Communication Science)|
|subject leader||Prof. Dr. Emese Domahidi|
|on-campus program (h)||67|
|exam||alternative examination performance|
|details of the certificate|
Students are required to conduct their own research, present the findings, and write a term paper based on their work during the two semesters (see more in the description below)
|maximum number of participants||20|
|previous knowledge and experience|
Familiarity with quantitative methods in communication research. R skills are a plus, however, not mandatory.
In 2015, the image-recognition technology of Google Photos, an app that stores and organizes photos of its users, labeled a photo of an African-American and his girlfriend as “gorillas” sparking a massive public outcry. This offensive incident is just one of the examples of stereotypes, biases towards a certain social group, that can be found within the boundlessness of the Internet in images, videos, and text. Although stereotypes are nothing new, with the rise of social networks and digital media researchers have an opportunity to trace and analyze them in order to de-bias online content.
In this course, we will have a close look at some of the common stereotypes that are present in various texts in digital media. In the winter semester, students will review previous literature on biases in digital media as well as biases demonstrated by machines and artificial intelligence. Moreover, in order to analyze the topic students will get acquainted with relevant computational approaches of content analysis. On the basis of recommended readings participants will discuss findings, theoretical perspectives and applied methods. Students will work in small research groups and develop own research questions based on earlier literature.
Assessment winter semester: Students will review the literature, develop research questions and write a term paper describing their planned research (pre-registration).
In the summer semester, students will apply computational methods for the purposes of their studies. Introduction to R for data management and analysis will be provided beforehand. Finally, students will analyze collected data and present their results to their peers.
Assessment summer semester: Research groups will analyze their data, present results and conclusions, and write a term paper.
|media of instruction|
|literature / references|
|evaluation of teaching|
WS 2017/18 (Seminar)