On this page, you’ll find all materials (e.g., slides, handouts,
R-syntax, etc.) for this course.
Preparation
Install R and RStudio
Before the first practical session, please make sure that you have R
and Rstudio installed on your computer and have played around a little
with it (it is open source and free to use). Please work through the
initial steps outlined in these two worksheets:
Familiarize yourself with the course
Lectures are every Monday from 13.30-15.15 (see overview for exact dates and times). In these
lectures, a particular research problem will be introduced and we will
discuss methodological solutions. We will get to know respective
computational methods and contextualize them within the broader research
process. Practical sessions are every **Tuesday* and Thursday
between 9-10.45 or 11-12.45 (again see overview for exact dates and times). For the
practical sessions, the class will be split into 6 groups of equal size.
In these practical sessions, we will work with RStudio and ran analyses
on real-world data sets. We therefore engage in various data analytic
exercises. We expect you to:
- Bring your own computer
- Run the scripts and work through the code
- Ask questions and participate!
In the third part of the course, students will work on their own
research projects. Resources and material for additional methods and
statistical approaches will be provided to all students.
Week 1
Lecture: Introduction to Computational Methods in Communication
Science
Practical Session 1: Data Wrangling
Practical Session 2: Data visualization
Homework
- Work through this tutorial:
- Complete this assignment (available on canvas in the modules
“Homework Assignments” & “Data Sets)
- 01_homework_assignment.rmd
Week 2
Lecture: Automated Text Analysis and Dictionary Approaches
- Slides Lecture 2:
- [Download Slides] (not yet available)
Practical Session 1
- Handouts:
- Basic Text Analysis using tidytext (not yet available)
Practical Session 2
Handouts:
- Dictionary Approaches using tidytext (not yet available)
Homework
- Complete this assignment (available on canvas in the module
“Homework Templates & Datasets)
- 02_homework_assignment.rmd
Week 3
Lecture: Text Classification and Classic Machine Learning
- Slides Lecture 3:
- [Download Slides] (not yet available)
Practical Session 1
- Handouts:
- Supervised text classification using Naive Bayes and Neural
Networks
Practical Session 2
- Handouts:
- Supervised text classification with word-embeddings and neural
networks (not yet available)
Homework
- Complete this assignment (available on canvas in the module
“Homework Templates & Datasets)
- 03_homework_assignment.rmd
Week 4
Practical Session 1
- Handouts:
- Zero-Shot Text classification using Transformers/GPT/LLama (not yet
available)
Practical Session 2
- Handouts:
- Few-Shot Classification using GPT/Llama (not yet available)
Homework
- Complete this assignment (available on canvas in the module
“Homework Templates & Datasets)
- 04_homework_assignment.rmd
Week 5
Exam week
- Date and Time of the Exam
- Date: 29th November 2024
- Time: 08:30am
- Location: t.b.a
- Make sure to be there on time
- Format
- The exam will be on TestVision
- It consists of Multiple Choice Questions & Open Questions
- Example questions are provided at the end of all lecture slides
- What do you need to know?
- Content of the lectures
- The required readings (see lecture slides and Canvas)
- The practical sessions will further help to deepen your
understanding of the concepts and approaches (e.g., how to interpret
results). While you will not be required to write R code in the exam, we
expect you to be able to read it and interpret R output.
Week 6 & 7
Introduction to Group Research Projects
Additional methods and resources (handouts and video tutorials)
- A general collection of diverse R tutorials
- Basic Statistics in R
- Test theory and factor analyses
- Advanced Statistics
- Additional text analysis
- Web Scraping
Week 8
Final presentation at the “mini-conference”
This course is published under the following license.