DA110: Computational Analysis of Communication
Mannheim Master in Data Science — Spring Term 2024
CCS
Exercise
Data Science
Schedule
Note: This course website might be updated throughout the semester so please check regularly!
What is it about?
As “big data” and “algorithms” affect our daily communication, new research questions arise at the intersection between societies and technologies. Many of these questions are of great social relevance and are therefore prominently discussed both by researchers and in the media. One outstanding, recent example from the field of media psychology is a rising interest in the association of (social) media use and mental-health. Another example, from the realm of political communication, is the ongoing debate about the role of new communication technologies during political campaigns (e.g., to spread disinformation). Both questions revolve around the process of communication. Sound research in this area thus requires both a solid foundation from communication theory as well as expertise in handling new and “big” data. To close this gap, the growing discipline of Computational Communication Science (CCS) takes on a combinatory perspective between social and computer science. The present course will provide an overview about the current state of CCS and intends to motivate students to approach pressing social questions from a different perspective.
What will I learn?
Expertise:
After the course, you will be aware of the typical research topics and questions in CCS and the different methodological approaches for tackling them (e.g., automated media content analysis); you will know the different methods’ potentials, limitations, and typical fields of application; you will be able to develop your own specific research questions and will be able to make an informed decision about which method to apply for answering it.
Methodological competence:
You will be able to independently develop a research question and design in the area of computational communication science based on communication theory; you will be able to conduct analyses using one of the methodological approaches introduced in the exercises; you will be able to document the results of your analyses in a research report and reflect upon your findings’ limitations with regard to reliability and validity.
Personal competence:
The course supports you in developing problem-solving competences with regard to research-design oriented questions. By solving exercises independently, your ability to transfer the learned material to related questions is promoted and you will be confident to tackle your own research-oriented tasks in the future.
What will I do?
The class will be conduct as a seminar block over one weekend, with additional kick-off and closing sessions. Sessions will not be streamed via Zoom (unless otherwise communicated) and will not be recorded. Most course materials will be provided through this course website. However, all students participating in the class need to request access to the virtual learning space on the ILIAS platform, because confidential class materials will be provided via ILIAS and exercises have to be submitted there.
All students wishing to receive credit for the class are obliged to engage in the following two types of regular performance. These tasks will not be graded. However: If you fail to engage in these tasks, you cannot pass the class.
Prepare for the live sessions: To prepare for a live session, please refer to the “prepare” column in the course overview. For example, you may be required to read some literature, and/or you will be provided with a few questions. You should be able to provide an answer to these questions in class.
Exercises: To practice your competence with the methods that are covered in this class, you will sometimes be provided with practical exercises at the end of a lesson (see also the “exercise” column in the course overview). These exercises have to be submitted by all students via ILIAS.