The course consists of one lecture and two practical sessions per week. For the practical sessions, the group will be split into 5-6 groups of equal size.
| Class | Teacher | Time |
|---|---|---|
| Lectures | Wouter van Atteveldt | Monday 13:30 |
| Practical Session 1 (CS/CW) | Gianni Quaedvlieg | Tuesday, 9:00 & Thursday, 11:00 |
| Practical Session 3 (AI) | Alberto López-Ortega | Tuesday, 9:00 & Thursday, 11:00 |
| Practical Session 5 (CS/CW) | Kasper Welbers | Tuesday, 11:00 & Thursday, 09:00 |
| Practical Session 6 (CW) | Gianni Quaedvlieg | Tuesday, 11:00 & Thursday, 09:00 |
| Practical Session 7 (AI) | Sofia Gil-Clavel | Tuesday, 11:00 & Thursday, 09:00 |
See the schedule website for rooms
Sessions 3 and 7 (AI) are reserved for students AI & Governance. Sessions 1 and 5 (CS/CW) are bilingual sessions in the English and Dutch tracks of communication science. Of course, students in the Dutch track are free to ask questions and communicate with the teacher in Dutch. Session 6 (CW) is in Dutch only. Sessions 2, 4 and 8 have been merged into other groups due to lower student numbers than expected.
| Day | Week | Type | Content | Assignment |
|---|---|---|---|---|
| Part I: Introduction and Automated Text Analysis | ||||
| Monday | 1 | Lecture | Introduction to Computational Methods in Communication Science | |
| Tuesday | 1 | Practical session | Data Wrangling using the tidyverse and tidytext | |
| Thursday | 1 | Practical session | Exploratory Data Analysis and Data Visualization | Homework Assignment 1 |
| Monday | 2 | Lecture | Automated Text Analysis and Dictionary Approaches | |
| Tuesday | 2 | Practical session | Basic Text Analysis using tidytext | |
| Thursday | 2 | Practical session | Dictionary Approaches using tidytext | Homework Assignment 2 |
| Part II: Text Classification using Machine Learning | ||||
| Monday | 3 | Lecture | Text Classification Using Machine Learning | |
| Tuesday | 3 | Practical session | Supervised text classification using Naive Bayes and neural networks | |
| Thursday | 3 | Practical session | Supervised text classification with wordembeddings and neural networks | Homework Assignment 3 |
| Monday | 4 | Lecture | Transformers and Large Language Models | |
| Tuesday | 4 | Practical session | Zero-Shot Classification Using Transformers/GPT/llama | |
| Thursday | 4 | Practical session | Few-Shot Classification Using GPT/llama | Homework Assignment 4 |
| Exam week | ||||
| Friday | 5 | Exam | Multiple-Choice Exam (Content of Part I & II) | |
| Part III: Practical Group Projects | ||||
| Monday | 6 | Lecture | Summary and Introduction to Group Projects | |
| Tuesday | 6 | Practical session | Meeting with supervisor | |
| Thursday | 6 | Practical session | Meeting with supervisor | |
| Monday | 7 | No lecture | ||
| Tuesday | 7 | Practical session | Meeting with supervisor | |
| Thursday | 7 | Practical session | Meeting with supervisor | |
| Tuesday | 8 | Conference | Presentation of Group Projects | 10-min talk |
This course is published under the following license.