A crash course on content analysis

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Daniela Crăciun, Bard College Berlin

An ever-increasing proportion of political, social and cultural activity is recorded as digital text. How can we analyze these texts to answer salient questions about the societies we live in? This short workshop aims to survey a family of research methods for systematically extracting information from textual data for scientific purposes known as content analysis. It will cover different approaches to text analysis: from traditional manual content analysis to contemporary state of the art computer-assisted content analysis methods. Using empirical research from a variety of fields, participants will get acquainted with both the theoretical groundings of content analysis methods and how to apply them in practical research projects. By the end of the workshop, participants will be able to categorize, compare and use different approaches to text analysis.

Session 1: An Introduction to Content Analysis Research Design

This session will introduce participants to the family of methods known as content analysis and cover the fundamentals of a content analysis research design.

Readings

Grimmer, Justin, and B.M. Stewart. 2013. “Text as Data: The Promise and Pitfalls of Automated Content Analysis Methods for Political Texts.” Political Analysis 21(3): 1–31.

Wilkerson, John, and Andreu Casas. 2017. “Large-Scale Computerized Text Analysis in Political Science: Opportunities and Challenges.” Annual Review of Political Science 20(1): 529–44.

Benoit, Kenneth. 2019. “Text as Data : An Overview.” In The SAGE Handbook of Research Methods in Political Science and International Relations, eds. Luigi Curini and Robert Franzese. London: Sage Publications.

 

Session 2: Applications of Computer Assisted Content Analysis

This session will introduce participants to empirical applications of computer assisted content analysis.

Readings

Laver, Michael, Kenneth Benoit, and John Garry. 2003. “Extracting Policy Positions from Political Texts Using Words as Data.” American Political Science Review 97(2): 311–31.

Slapin, Jonathan B, and Sven-oliver Proksch. 2008. “A Scaling Model for Estimating Time-Series Party Positions from Texts.” American Journal of Political Science 52(3): 705–22.

Blei, D.M., A.Y. Ng, and M.I. Jordan. 2003. “Latent Dirichlet Allocation.” Journal of Machine Learning Research 3: 993–1022.

Reference Books

Krippendorff, K. 2004. Content Analysis: An Introduction to Its Methodology. Thousand Oaks, London, New Delhi: Thousand Oaks.

 

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