LEADER 03529nam 22005775 450 001 9911008493603321 005 20250604125751.0 010 $a3-658-47798-9 024 7 $a10.1007/978-3-658-47798-1 035 $a(CKB)39260658700041 035 $a(MiAaPQ)EBC32154191 035 $a(Au-PeEL)EBL32154191 035 $a(DE-He213)978-3-658-47798-1 035 $a(OCoLC)1524429716 035 $a(EXLCZ)9939260658700041 100 $a20250604d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAutomated Detection of Media Bias $eFrom the Conceptualization of Media Bias to its Computational Classification /$fby Timo Spinde 205 $a1st ed. 2025. 210 1$aWiesbaden :$cSpringer Fachmedien Wiesbaden :$cImprint: Springer Vieweg,$d2025. 215 $a1 online resource (308 pages) 311 08$a3-658-47797-0 327 $aIntroduction -- Media Bias -- Questionnaire Development -- Dataset Creation -- Feature-based Media Bias Detection -- Neural Media Bias Detection -- Visualization and Perception of Media Bias -- Conclusion and FutureWork. 330 $aThis 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. 606 $aApplication software 606 $aComputer simulation 606 $aCommunication 606 $aInformation theory 606 $aComputer and Information Systems Applications 606 $aComputer Modelling 606 $aMedia and Communication Theory 615 0$aApplication software. 615 0$aComputer simulation. 615 0$aCommunication. 615 0$aInformation theory. 615 14$aComputer and Information Systems Applications. 615 24$aComputer Modelling. 615 24$aMedia and Communication Theory. 676 $a005.3 700 $aSpinde$b Timo$01827321 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911008493603321 996 $aAutomated Detection of Media Bias$94395476 997 $aUNINA