Introduction to Automated Content Analysis in R

We are finally getting to the method of our choice: automated content analysis.

Now that you’ve mastered the basics of R, it will be much easier for you to understand the logic of and commands for automated content analysis in R

As a recommendation (you’ll also find this information on the syllabus): The following texts and tutorials are really helpful for further understanding the method:

Texts:

  • van Atteveldt, W., Welbers, K., & van der Velden, M. (2019). Studying Political Decision Making with Automatic Text Analysis. In W. van Atteveldt, K. Welbers, & M. van der Velden, Oxford Research Encyclopedia of Politics. Oxford University Press. Link

  • Benoit, K. (2019). Text as data: An overview. In Cuirini, L., & Franzese, R. (Eds.), Handbook of Research Methods in Political Science and International Relations. Preprint

  • Boumans, J. W., & Trilling, D. (2016). Taking Stock of the Toolkit: An overview of relevant automated content analysis approaches and techniques for digital journalism scholars. Digital Journalism, 4(1), 8–23. Link

  • Denny, M., & Spirling, A. (2018). Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It. Political Analysis, 26(2), 168-189. Link

  • DiMaggio, P. (2015). Adapting computational text analysis to social science (and vice versa). Big Data & Society. Link

  • Grimmer, J., & Stewart, B. M. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(3), 267–297. Link

  • Günther, E., & Quandt, T. (2016). Word Counts and Topic Models: Automated text analysis methods for digital journalism research. Digital Journalism, 4(1), 75–88. Link

  • Hase, V. (in press). Automated Content Analysis. In F. Oehmer, S. H. Kessler, E. Humprecht, K. Sommer, & L. Castro Herrero (Eds.), Handbook of Standardized Content Analysis: Applied Designs to Research Fields of Communication Science. VS Springer. (accessible via OLAT)

  • Lucas, C., Nielsen, R. A., Roberts, M. E., Stewart, B. M., Storer, A., & Tingley, D. (2015). Computer-Assisted Text Analysis for Comparative Politics. Political Analysis, 23(2), 254–277. Link

  • Quinn, K. M., Monroe, B. L., Colaresi, M., Crespin, M. H., & Radev, D. R. (2010). How to Analyze Political Attention with Minimal Assumptions and Costs. American Journal of Political Science, 54(1), 209–228. Link

  • Welbers, K., Van Atteveldt, W., & Benoit, K. (2017). Text Analysis in R. Communication Methods and Measures, 11(4), 245–265. Link

  • Wilkerson, J., & Casas, A. (2017). Large-Scale Computerized Text Analysis in Political Science: Opportunities and Challenges. Annual Review of Political Science, 20(1), 529–544. Link

  • Zamith, R., & Lewis, S. C. (2015). Content Analysis and the Algorithmic Coder: What Computational Social Science Means for Traditional Modes of Media Analysis. The ANNALS of the American Academy of Political and Social Science, 659(1), 307–318. Link

Tutorials:

  • Bail, C. Day 3: Automated Text Analysis. Link

  • Bernauer J, & Traber D. Quantitative Analysis of Political Text. Link

  • Sanchez, G. (2014). Handling and Processing Strings in R. Link

  • Silge, J., & Robinson, D. Text mining with R: A tidy approach. Link

  • Puschmann, C., & Haim, R. Automated Content Analysis with R. Link

  • Unkel, J. (2020). Methodische Vertiefung: Computational Methods mit R und R Studio. Link

  • Watanabe, K., & Müller, S (2021). Quanteda Tutorials. Link

Let’s keep going: Tutorial 9: Searching & manipulating string patterns.