The computational content analyst: using machine learning to classify media messages
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 Py...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
New York ; London
Routledge, Taylor & Francis Group
2025
|
| Ausgabe: | First published |
| Schlagwörter: | |
| Zusammenfassung: | 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 |
| Beschreibung: | 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. Unveiling the Veiled: 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 |
| Umfang: | ix, 133 Seiten |
| ISBN: | 9781032846309 9781032846354 |
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| 520 | |a 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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| spelling | Vargo, Chris J. Verfasser (DE-588)1080910026 aut The computational content analyst using machine learning to classify media messages Chris J. Vargo First published New York ; London Routledge, Taylor & Francis Group 2025 ix, 133 Seiten txt rdacontent n rdamedia nc rdacarrier 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. Unveiling the Veiled: 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 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 bicssc / Media studies bicssc / Research methods - general bicssc / Humanities bisacsh / SOCIAL SCIENCE / Methodology bisacsh / SOCIAL SCIENCE / Media Studies Erscheint auch als Online-Ausgabe 978-1-00-351423-7 |
| spellingShingle | Vargo, Chris J. The computational content analyst using machine learning to classify media messages bicssc / Media studies bicssc / Research methods - general bicssc / Humanities bisacsh / SOCIAL SCIENCE / Methodology bisacsh / SOCIAL SCIENCE / Media Studies |
| title | The computational content analyst using machine learning to classify media messages |
| title_auth | The computational content analyst using machine learning to classify media messages |
| title_exact_search | The computational content analyst using machine learning to classify media messages |
| title_full | The computational content analyst using machine learning to classify media messages Chris J. Vargo |
| title_fullStr | The computational content analyst using machine learning to classify media messages Chris J. Vargo |
| title_full_unstemmed | The computational content analyst using machine learning to classify media messages Chris J. Vargo |
| title_short | The computational content analyst |
| title_sort | the computational content analyst using machine learning to classify media messages |
| title_sub | using machine learning to classify media messages |
| topic | bicssc / Media studies bicssc / Research methods - general bicssc / Humanities bisacsh / SOCIAL SCIENCE / Methodology bisacsh / SOCIAL SCIENCE / Media Studies |
| topic_facet | bicssc / Media studies bicssc / Research methods - general bicssc / Humanities bisacsh / SOCIAL SCIENCE / Methodology bisacsh / SOCIAL SCIENCE / Media Studies |
| work_keys_str_mv | AT vargochrisj thecomputationalcontentanalystusingmachinelearningtoclassifymediamessages |