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ANU Centre for Social Research Methods · ANU College of Arts and Social Sciences
The course provides an introduction to the analysis of text as data. It explores how to implement cutting edge text analysis methods using the statistical programing language R, and how to design and execute your own social science research project that uses text data analysis as a key component.
Six homework problem sets using the R programming language on the concepts and techniques discussed each week (5% each, dropping the lowest marked set, 25% in total)
Learning outcomes 1
Research proposal, 1000 words
Learning outcomes 2, 3
Research project, 3000 words
Learning outcomes 2, 3
130 hours of total student learning time made up from: a) 36 hours of contact over 12 weeks: 18 hours of lectures and 18 hours of tutorials/computer training sessions; and b) 94 hours of independent student research, reading and writing.
Krippendorff, Klaus. 2013. Content Analysis: An Introduction to Its Methodology. Sage. Preliminary Reading Benoit, Kenneth, Conway, Drew, Lauderdale, Benjamin E., Laver, Michael and Mikhaylov, Slava. 2015. "Crowdsourced text analysis: reproducible and agile production of political data." American Political Science Review. Krippendorff, Klaus. 2013. Content Analysis: An Introduction to Its Methodology. Sage. Lazer, David, Ryan Kennedy, Gary King and Alessandro Vespignani. 2014. “The Parable of Google Flu: Traps in Big Data Analysis.” Science 343. Leskovec, Jure and Backstrom, Lars and Kleinberg, Jon. 2009. "Meme-tracking and the Dynamics of the News Cycle." Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Roberts, Carl W. 2000. “A Conceptual Framework for Quantitative Text Analysis.” Quality and Quantity 34(3): 259—274.