Automated Detection of Media Bias
This Open Access book explores the automated identification of media bias, particularly focusing on bias by word choice in digital media. The increasing prevalence of digital information presents opportunities and challenges for analyzing language, with cultural, geographic, and contextual factors s...
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| Formatua: | Online |
| Hizkuntza: | ingelesa |
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Springer Nature
2025
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| Gaiak: | |
| Sarrera elektronikoa: | ONIX_20250613T105552_9783658477981_28 |
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Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
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| _version_ | 1869522517231665152 |
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| author | Spinde, Timo |
| author_browse | Spinde, Timo |
| author_facet | Spinde, Timo |
| author_sort | Spinde, Timo |
| collection | Directory of Open Access Books |
| description | This Open Access book explores the automated identification of media bias, particularly focusing on bias by word choice in digital media. The increasing prevalence of digital information presents opportunities and challenges for analyzing language, with cultural, geographic, and contextual factors shaping how content is portrayed. Despite the interdisciplinary nature of media bias research across fields like linguistics, psychology, and computer science, existing work often tackles the problem from limited perspectives, lacking comprehensive frameworks and reliable datasets. The book aims to advance the field by addressing these gaps and proposing a systematic approach to media bias detection. It develops feature-based and deep-learning approaches for automated bias detection, including a BERT-based model and MAGPIE, a multi-task learning model. These methods demonstrate improved performance on established benchmarks, showcasing the potential of deep learning in detecting media bias. Finally, the author addresses the practical applications of automated bias detection, such as enhancing news reading with forewarning messages, text annotations, and political classifiers, and examines the impact of bias on social media engagement. |
| format | Online |
| id | doab-20.500.12854ir-161358 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Springer Nature |
| publisherStr | Springer Nature |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1613582025-06-14T05:07:02Z Automated Detection of Media Bias Spinde, Timo automated bias analysis bias by word choice media bias datasets deep learning in media analysis interdisciplinary bias research bias perception assessment automatic bias identification bias in news reporting media bias framework media bias detection thema EDItEUR::U Computing and Information Technology::UX Applied computing thema EDItEUR::U Computing and Information Technology::UY Computer science::UYM Computer modelling and simulation 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::GT Interdisciplinary studies::GTC Communication studies This Open Access book explores the automated identification of media bias, particularly focusing on bias by word choice in digital media. The increasing prevalence of digital information presents opportunities and challenges for analyzing language, with cultural, geographic, and contextual factors shaping how content is portrayed. Despite the interdisciplinary nature of media bias research across fields like linguistics, psychology, and computer science, existing work often tackles the problem from limited perspectives, lacking comprehensive frameworks and reliable datasets. The book aims to advance the field by addressing these gaps and proposing a systematic approach to media bias detection. It develops feature-based and deep-learning approaches for automated bias detection, including a BERT-based model and MAGPIE, a multi-task learning model. These methods demonstrate improved performance on established benchmarks, showcasing the potential of deep learning in detecting media bias. Finally, the author addresses the practical applications of automated bias detection, such as enhancing news reading with forewarning messages, text annotations, and political classifiers, and examines the impact of bias on social media engagement. 2025-06-14T05:07:01Z 2025-06-14T05:07:01Z 2025-06-13T09:20:42Z 2025 book ONIX_20250613T105552_9783658477981_28 https://library.oapen.org/handle/20.500.12657/103579 9783658477981 9783658477974 https://directory.doabooks.org/handle/20.500.12854/161358 eng open access image/jpeg n/a https://library.oapen.org/bitstream/20.500.12657/103579/1/9783658477981.pdf Springer Nature Springer Fachmedien Wiesbaden 10.1007/978-3-658-47798-1 10.1007/978-3-658-47798-1 9fa3421d-f917-4153-b9ab-fc337c396b5a 72be78ea-d018-45fd-9a52-17b8f8ba806e c5bf50ae-74c7-4060-979f-ebef8fa9a8df 9783658477981 9783658477974 Springer Fachmedien Wiesbaden 246 Wiesbaden [...] [...] open access |
| spellingShingle | automated bias analysis bias by word choice media bias datasets deep learning in media analysis interdisciplinary bias research bias perception assessment automatic bias identification bias in news reporting media bias framework media bias detection thema EDItEUR::U Computing and Information Technology::UX Applied computing thema EDItEUR::U Computing and Information Technology::UY Computer science::UYM Computer modelling and simulation 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::GT Interdisciplinary studies::GTC Communication studies Spinde, Timo Automated Detection of Media Bias |
| title | Automated Detection of Media Bias |
| title_full | Automated Detection of Media Bias |
| title_fullStr | Automated Detection of Media Bias |
| title_full_unstemmed | Automated Detection of Media Bias |
| title_short | Automated Detection of Media Bias |
| title_sort | automated detection of media bias |
| topic | automated bias analysis bias by word choice media bias datasets deep learning in media analysis interdisciplinary bias research bias perception assessment automatic bias identification bias in news reporting media bias framework media bias detection thema EDItEUR::U Computing and Information Technology::UX Applied computing thema EDItEUR::U Computing and Information Technology::UY Computer science::UYM Computer modelling and simulation 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::GT Interdisciplinary studies::GTC Communication studies |
| topic_facet | automated bias analysis bias by word choice media bias datasets deep learning in media analysis interdisciplinary bias research bias perception assessment automatic bias identification bias in news reporting media bias framework media bias detection thema EDItEUR::U Computing and Information Technology::UX Applied computing thema EDItEUR::U Computing and Information Technology::UY Computer science::UYM Computer modelling and simulation 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::GT Interdisciplinary studies::GTC Communication studies |
| url | ONIX_20250613T105552_9783658477981_28 |
| work_keys_str_mv | AT spindetimo automateddetectionofmediabias |