The Computational Content Analyst
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...
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| Format: | Online |
| Jezik: | engleski |
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Taylor & Francis
2025
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| Teme: | |
| Online pristup: | ONIX_20250530T083217_9781040227176_20 |
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Bez oznaka, Budi prvi tko označuje ovaj zapis!
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| _version_ | 1869527446949199872 |
|---|---|
| author | Vargo, Chris J. |
| author_browse | Vargo, Chris J. |
| author_facet | Vargo, Chris J. |
| author_sort | Vargo, Chris J. |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-160957 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Taylor & Francis |
| publisherStr | Taylor & Francis |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1609572025-05-31T06:04:06Z The Computational Content Analyst Vargo, Chris J. communication studies computational social science mass communication big data machine learning artificial intelligence large language models thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GT Interdisciplinary studies::GTC Communication studies thema EDItEUR::J Society and Social Sciences::JB Society and culture: general::JBC Cultural and media studies::JBCT Media studies thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPS Research methods: general thema EDItEUR::N History and Archaeology::NH History 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. 2025-05-31T06:04:06Z 2025-05-31T06:04:06Z 2025-05-30T06:42:05Z 2024 book ONIX_20250530T083217_9781040227176_20 https://library.oapen.org/handle/20.500.12657/103064 9781040227176 9781032846354 9781032846309 9781003514237 9781040227206 https://directory.doabooks.org/handle/20.500.12854/160957 eng open access image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International https://library.oapen.org/bitstream/20.500.12657/103064/1/9781040227176.pdf Taylor & Francis Routledge 10.4324/9781003514237 10.4324/9781003514237 fa69b019-f4ee-4979-8d42-c6b6c476b5f0 dacf1b91-1b1b-466d-a935-d8daeac1f52c 9432cec5-f7e0-412c-8b5c-d5659a7cb5b7 9781040227176 9781032846354 9781032846309 9781003514237 9781040227206 Routledge 144 Oxford [...] University of Colorado Colorado University 10.13039/100010174 open access |
| spellingShingle | communication studies computational social science mass communication big data machine learning artificial intelligence large language models thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GT Interdisciplinary studies::GTC Communication studies thema EDItEUR::J Society and Social Sciences::JB Society and culture: general::JBC Cultural and media studies::JBCT Media studies thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPS Research methods: general thema EDItEUR::N History and Archaeology::NH History Vargo, Chris J. The Computational Content Analyst |
| title | The Computational Content Analyst |
| title_full | The Computational Content Analyst |
| title_fullStr | The Computational Content Analyst |
| title_full_unstemmed | The Computational Content Analyst |
| title_short | The Computational Content Analyst |
| title_sort | computational content analyst |
| topic | communication studies computational social science mass communication big data machine learning artificial intelligence large language models thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GT Interdisciplinary studies::GTC Communication studies thema EDItEUR::J Society and Social Sciences::JB Society and culture: general::JBC Cultural and media studies::JBCT Media studies thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPS Research methods: general thema EDItEUR::N History and Archaeology::NH History |
| topic_facet | communication studies computational social science mass communication big data machine learning artificial intelligence large language models thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GT Interdisciplinary studies::GTC Communication studies thema EDItEUR::J Society and Social Sciences::JB Society and culture: general::JBC Cultural and media studies::JBCT Media studies thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPS Research methods: general thema EDItEUR::N History and Archaeology::NH History |
| url | ONIX_20250530T083217_9781040227176_20 |
| work_keys_str_mv | AT vargochrisj thecomputationalcontentanalyst AT vargochrisj computationalcontentanalyst |