The Computational Content Analyst Using Machine Learning to Classify Media Messages

• Main Author: Vargo, Chris J
• Format: eBook
• Language: English
• Published: Oxford Taylor & Francis 2024
• Subjects: artificial intelligence; big data; Communication Studies; computational social science; Cultural and media studies; History; History and Archaeology; Interdisciplinary studies; Language Arts & Disciplines; large language models; machine learning; mass communication; Media Studies; Methodology; Reference, Information and Interdisciplinary subjects; Research and information: general; Research methods: general; Social Science; Society and culture: general; Society and Social Sciences
• ISBN: 9781032846309; 9781040227206; 1032846305; 1032846356; 1040227171; 9781040227176; 9781003514237; 9781032846354; 1003514235; 1040227201
• DOI: 10.4324/9781003514237

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.