400023 SE Content Analysis in the Social Sciences: Human and Automated Approaches (2016W)
SE Methods for Doctoral Candidates
Continuous assessment of course work
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Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Th 03.11.2016 08:00 to Su 20.11.2016 00:00
- Deregistration possible until Fr 25.11.2016 00:00
Details
max. 21 participants
Language: English
Lecturers
Classes
MO 12.12.2016 09.00-11.00 Seminarraum 12, Währinger Straße 29 2.OG
DI 13.12.2016 9:45-13:00 Seminarraum, Rathausstraße 19/9, 1010 Wien
DI 13.12.2016 14.00-16.30 PC-Unterrichtsraum 1, Währinger Straße 29 1.UG
MI 14.12.2016 09.45-13.00 Seminarraum 8, Währinger Straße 29 1.OG
MI 14.12.2016 14.00-16.30 PC-Unterrichtsraum 6, Währinger Straße 29 2.OG
DO 15.12.2016 09.00-12.30 Seminarraum 9, Währinger Straße 29 2.OG
DO 15.12.2016 14.00-16.30 PC-Unterrichtsraum 1, Währinger Straße 29 1.UG
Information
Aims, contents and method of the course
Assessment and permitted materials
Minimum requirements and assessment criteria
Examination topics
Reading list
Association in the course directory
Last modified: Mo 07.09.2020 15:47
of scripts from movies and film, and from video games. Social media analysts have ready access to miles of data, particularly from Twitter. And that same content has been of interest to scholars interested in predicting election outcomes, macro-economic trends, and stock prices.Content analysis has been around for a long time, however; and many of the core ideas developed 40 years ago are equally important today. The objective of this short course is to offer a theoretically-informed introduction to large-scale content-analytic methods for the social sciences (albeit with an emphasis on political communication). We begin by reviewing past work on human-coded methods, and then proceed to current automated approaches, both dictionary based and machine learning.Classes combine lectures, discussion, and computer-based lab work. It is expected that students will have completed readings before class, and will have both R, RStudio, and Lexicoder installed on their computers. Please bring your own computers with you to the course. This software is free, and multi-platform; we will
be using it for content analysis of texts. If students are interested in working with particular corpuses, they are welcome to bring those to class; otherwise, example corpuses will be provided. By the end of the class, students will be familiar with a range of content analytic approaches, and will be able to conduct automated analyses of
large-scale corpuses in R.