​​RSS02.E5 Introduction to Text as Data​ - Confirmed

Confirmed

Facing the massive volumes of text data that are available in digital format and
valuing their potential, over recent years social scientists have increasingly turned
to methods that rely on the support of computer power, so- called automated text analysis methods. The text-as-data methods are used to draw reproducible and valid inferences or meanings from documents. As an enhancement of the more classical manual methods of content analysis, automated methods of text analysis are becoming prevalent in disciplines that are overall increasingly computationally oriented. 

This course provides an introduction to various text as data methods. It includes aspects related to data collection, data processing, quality control, and the critical interpretation of results.  In detail, the following topics are covered:  Motivations and applications for using text as data methods, Data collection, Data selection, Feature Selection, Text Representation, Rule-based classification  Supervised classification, Unsupervised approaches, Intro advanced methods (e.g., Scaling methods, Embeddings, Multilingual text analysis). All topics are introduced with a lecture type approach and then illustrated with practical examples.

The lecture part consists of input by the instructor (i.e., covering the basics of each topic, highlighting latest methods research, and introducing resources) and shorter interactive parts (i.e., reflection and discussions on different methods in plenary and small groups). The practical part consists of guided coding sessions, where we work together through prepared code. In addition, small coding challenges (1h), which are worked on alone or in groups after the class hours will be assigned Monday, Tuesday, Wednesday, and Thursday.