Most digital content, whether it be thousands of news articles or millions
of social media posts, is too large for the naked eye alone. Often, the advent
of immense datasets requires a more productive approach to labeling
media beyond a team of researchers. This book offers practical guidance
and Python code to traverse the vast expanses of data—significantly
enhancing productivity without compromising scholarly integrity. We’ll
survey a wide array of computer-based classification approaches, focusing
on easy-to-understand methodological explanations and best practices to
ensure that your data is being labeled accurately and precisely. By reading
this book, you should leave with an understanding of how to select the best
computational content analysis methodology to your needs for the data and
problem you have.

This guide gives researchers the tools they need to amplify their analytical
reach through the integration of content analysis with computational
classification approaches, including machine learning and the latest
advancements in generative artificial intelligence (AI) and large language
models (LLMs). It is particularly useful for academic researchers looking
to classify media data and advanced scholars in mass communications
research, media studies, digital communication, political communication,
and journalism.

Complementing the book are online resources: datasets for practice,
Python code scripts, extended exercise solutions, and practice quizzes for
students, as well as test banks and essay prompts for instructors. Please visit
www.routledge.com/9781032846354.