Upcoming book:
Computational Analysis of Communication:
A practical introduction to the analysis of texts, networks, and images with code examples in Python and R

Wouter van Atteveldt, Damian Trilling, Carlos Arcila; to be published late 2021

[Description | Highlights | Table of Contents]

This book is aimed at students and (aspiring) practitioners of computational social science, especially related to the analysis of digital communication. With this book and the accompanying code examples and video tutorials, you can learn data wrangling, machine learning, text analysis, and more. All examples are given side-by-side in Python and R, allowing you to pick your favorite language but also showing the differences and many similarities at a glance.

Uniquely, the full text of the book, including explanation and code examples, will be released on this website as soon as it hits the market. Please contact me if you have any questions or would like to have a sneak preview.

[Description | Highlights | Table of Contents]

Longer Description

Computational methods are rapidly gaining popularity in social science disciplines such as sociology, political science, psychology, (political) communication and media studies and are increasingly becoming part of the curriculum. Yet, there is no textbook that teaches in a practical way how computational methods originating from computer science, data science, and computer linguistics can be used within these disciplines.

The book uses an integrated approach to teach computational techniques in the context of social science and communication studies questions. Methods and code examples are directly linked to social science theory and cases from the literature. All techniques in the book are explained with code examples in both Python and R. The book teaches data analytics, computational thinking, and programming skills that have broad value in the private sector and within academia.

Students can use the book to learn useful techniques for analysing data that are relevant to the social sciences such as data management and visualization and text analysis. Students do not need to have programming experience, but can pick up the needed programming skills while working with the provided examples. As far as possible, all examples are linked to relevant societal questions, communication processes and social science theory and show how the techniques can be used to answer interesting and relevant questions. The book can be used cover-to-cover and will give students a very thorough introduction to computational social science, but it is also possible to use only relevant chapters.

[Description | Highlights | Table of Contents]

Some Highlights:

Gentle Introduction to using Python and R with side-by-side examples

Data visualization

Natural Language Processing

Network analysis

Dealing with visual data

Explainers for language-specific features

Best practices and recommendations

[Description | Highlights | Table of Contents]

Table of Contents

Part 1: Getting started

Part 2: Cleaning and analyzing data

Part 3: Text Analysis

Part 4: Beyond Structured Data

Part 5: Next Steps