The Computational Content Analyst
Using Machine Learning to Classify Media Messages
- 140 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
About this book
Most digital content, whether it be thousands of news articles, or millions of social media posts, are too large for the naked eye alone. Often, the advent of immense data sets requires a more productive approach to labelling 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 away of computer-based classification approaches, focusing on easy-to-understand methodological explanations and best practices to ensure that your data is being labelled 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 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.
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Information
Table of contents
- Cover
- Half Title
- Endorsements
- Title
- Copyright
- Dedication
- Contents
- Preface
- 1 Unveiling Content Analysis in the Contemporary Media Ecosystem
- 2 Designing a Computational Content Analysis: An Illustration from “Civic Engagement, Social Capital, and Ideological Extremity”
- 3 Basic Information Retrieval for Content Analysis
- 4 Supervised Machine Learning with BERT for Content Analysis
- 5 Text Classification of News Media Content Categories Using Deep Learning
- 6 Leveraging Generative AI for Content Analysis
- 7 Topic Modeling as a Lens for Discovery
- 8 Extending Deep Learning to Image Content Analysis
- Appendix A: Codebook and Conceptual Definitions
- Appendix B: Deletion Themes
- Index