General information on the course

This online tutorial will accompany the seminar “News on and for Social Media”. It is part of the B.A. “Communication Research” (LMU, Spring 2023). In short, it supports the group using automated content analysis for analyzing data donations.

What do I need this tutorial for?

This tutorial will introduce you to two main aspects:

  1. how to use R and R studio to conduct automated content analysis
  2. how to use R and R studio to do multivariate statistical analysis with such data

You are expected to work through the content of these tutorials before each of our regular sessions so we can use in-person meetings to discuss questions/test your new knowledge via exercises.

Each tutorial includes

  • introduction to new functions/analysis methods in R, including corresponding R code
  • information on other tutorials/sources on how to learn these methods
  • exercises which will help you understand and apply your new knowledge

Errrm - what if I have questions about all of this?

First of all: great! Questions will be one of the most important element of this seminar. Since you’ll be learning a lot of new things, it’s perfectly normal to have a lot of questions most of the time.

If you did not understand something in a tutorial, have a question about the syllabus, or just want me to repeat something: Please do ask! The most important thing when learning R is to understand that it is completely normal to feel lost sometimes. Don’t worry - it’s highly likely that everyone else feels the same.

Therefore, it’s key that you ask questions. There are three channels through which you may pose question you have (preferably in this order):

  1. In-person meetings on Tuesdays: Tuesdays, 2:00-6:00 pm.
  2. The Moodle forum: Outside of those sessions, please use the Moodle forum to ask any questions pertaining the seminar. This way, every participant will be able to see my answers and be provided with the same information. I recommend you turn on notifications about new entries in the forum to be informed about ongoing discussions.

Image: Moodle Forum

  1. Email: If you have specific questions about your own project etc. (or things you may not want to discuss with everyone in class), write me an email.

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: The following texts and tutorials are really helpful for further understanding the method:


  • 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

  • Hase, V. (2023). 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 (pp. 23–36). Link

  • 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

  • Welbers, K., Van Atteveldt, W., & Benoit, K. (2017). Text Analysis in R. Communication Methods and Measures, 11(4), 245–265. 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 (2023). Quanteda Tutorials. Link

That’s it - we’ll start right away with the first tutorial: Tutorial 1: Searching & manipulating string patterns.