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

Additional resources


Week 2

Lecture: Automated Text Analysis and Dictionary Approaches

Practical Session 1

Practical Session 2

Homework

  • Complete this assignment (available on canvas in the module “Homework Templates & Datasets)
    • 02_homework_assignment.rmd

Additional resources


Week 3

Lecture: Text Classification and Classic Machine Learning

Homework

  • Complete this assignment (available on canvas in the module “Homework Templates & Datasets)
    • 03_homework_assignment.rmd

Additional resources:


Week 4

Lecture: Transformers and Large Language Models

Homework

  • Complete this assignment (available on canvas in the module “Homework Templates & Datasets)
    • 04_homework_assignment.rmd

Additional resources:


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

Week 8

Final presentation at the “mini-conference”

  • At a final “mini-conference”, you will present the findings of your research projects!

  • Date: 16th December 2023

  • Time: 13.30 to 17.15

    • More information soon (including exact schedule)

This course is published under the following license.