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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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Gorde:
Egile nagusia: Spinde, Timo
Formatua: Online
Hizkuntza:ingelesa
Argitaratua: Springer Nature 2025
Gaiak:
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
Sarrera elektronikoa:ONIX_20250613T105552_9783658477981_28
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Gaia: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.

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