Automated Detection of Media Bias [electronic resource] : From the Conceptualization of Media Bias to its Computational Classification / by Timo Spinde.

За: Інтелектуальна відповідальність: Вид матеріалу: Текст Публікація: Wiesbaden : Springer Fachmedien Wiesbaden : Imprint: Springer Vieweg, 2025Видання: 1st ed. 2025Опис: XXVII, 246 p. 34 illus., 22 illus. in color. online resourceТип вмісту:
  • text
Тип засобу:
  • computer
Тип носія:
  • online resource
ISBN:
  • 9783658477981
Тематика(и): Додаткові фізичні формати: Printed edition:: Немає назви; Printed edition:: Немає назвиДесяткова класифікація Дьюї:
  • 005.3 23
Класифікація Бібліотеки Конгресу:
  • QA76.76.A65
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Вміст:
Introduction -- Media Bias -- Questionnaire Development -- Dataset Creation -- Feature-based Media Bias Detection -- Neural Media Bias Detection -- Visualization and Perception of Media Bias -- Conclusion and FutureWork.
У: Springer Nature eBookЗведення: 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. About the author Timo Spinde is a postdoctoral researcher specializing in media bias. He is the founder and coordinator of the Media Bias Group research network. He is affiliated with the University of Göttingen and the National Institute of Informatics (NII) in Tokyo.
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Introduction -- Media Bias -- Questionnaire Development -- Dataset Creation -- Feature-based Media Bias Detection -- Neural Media Bias Detection -- Visualization and Perception of Media Bias -- Conclusion and FutureWork.

Open Access

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. About the author Timo Spinde is a postdoctoral researcher specializing in media bias. He is the founder and coordinator of the Media Bias Group research network. He is affiliated with the University of Göttingen and the National Institute of Informatics (NII) in Tokyo.

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