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The Computational Content Analyst: Using Machine Learning to Classify Media Messages

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https://scholar.colorado.edu/concern/books/sq87bw357
Abstract
  •  
    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.

     

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  • 2025-08-11
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  • 9781003514237
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