Computational Content Analysis for the Social Sciences - SIMM71
7,5 CREDITS | AUTUMN TERM
This course is aimed towards students who have some prior knowledge of quantitative research methods and wish to further develop their understanding of content analysis, and ability to independently apply computational methods of extracting content features from digital texts and images.
The course focuses on content analysis as a method to gain knowledge about current and historical social issues by analyzing observations of communication and media messages. Some of the computational content analysis techniques most commonly used within the social sciences, such as natural language processing and computer vision, are presented and practiced, and their connection to the field of artificial intelligence and machine learning is discussed. The focus lies on applying these methods and techniques and presenting the results through data visualizations.
Teaching includes lectures and teacher assisted exercises in practical computational content analysis and data visualization (computer lab work).
Assessment is based on content analysis techniques for texts and images that are examined separately in two “data visualization reports” presented at seminars. The research reports should also include some contextual information, e.g. reflections on ethical aspects of the analysis techniques employed. Each report is examined individually and is worth 50% of the course grade.
Online course platform
This course uses Canvas as the online course platform. The course platform will be opened two weeks before the course begins to all students who were accepted. Here you will be able to access literature, assignments, announcements, as well as participate in discussions and communicate with teachers.
The course schedule can be found under the course information on the right. Please note that the final version of the schedule will be made available four weeks before the course begins, and changes may occur until that point. A more detailed schedule will be available on the course platform on Canvas.