Uncovering the essence of diverse media biases from the semantic embedding space.
Media bias widely exists in the articles published by news media, influencing their readers' perceptions, and bringing prejudice or injustice to society. However, current analysis methods usually rely on human efforts or only focus on a specific type of bias, which cannot capture the varying magnitu...
| Publicado en: | Humanities & Social Sciences Communications Vol. 11; no. 1; pp. 1 - 13 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
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Springer Nature
5/22/2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=177422968&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 177422968 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: MVR0 jtl: Humanities & Social Sciences Communications maglogo: N pubinfo: dt: 5/22/2024 vid: 11 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 177422968 10.1057/s41599-024-03143-w ppf: 1 ppct: 12 formats: tig: atl: Uncovering the essence of diverse media biases from the semantic embedding space. aug: au: Huang, Hong Zhu, Hua Liu, Wenshi Gao, Hua Jin, Hai Liu, Bang affil: National Engineering Research Center for Big Data Technology and System, Wuhan, China Services Computing Technology and System Lab, Wuhan, China Cluster and Grid Computing Lab, Wuhan, China School of Computer Science and Technology, Wuhan, China https://ror.org/00p991c53 Huazhong University of Science and Technology, Wuhan, China DIRO, Université de Montréal & Mila & Canada CIFAR AI Chair, Montreal, Canada su: Objectivity in journalism Natural language processing Russia-Ukraine Conflict, 2014- Semantic differential scale Differential psychology Prejudices Ukraine Russia sug: subj: Ukraine Russia Objectivity in journalism Natural language processing Russia-Ukraine Conflict, 2014- Semantic differential scale Differential psychology Prejudices ab: Media bias widely exists in the articles published by news media, influencing their readers' perceptions, and bringing prejudice or injustice to society. However, current analysis methods usually rely on human efforts or only focus on a specific type of bias, which cannot capture the varying magnitudes, connections, and dynamics of multiple biases, thus remaining insufficient to provide a deep insight into media bias. Inspired by the Cognitive Miser and Semantic Differential theories in psychology, and leveraging embedding techniques in the field of natural language processing, this study proposes a general media bias analysis framework that can uncover biased information in the semantic embedding space on a large scale and objectively quantify it on diverse topics. More than 8 million event records and 1.2 million news articles are collected to conduct this study. The findings indicate that media bias is highly regional and sensitive to popular events at the time, such as the Russia-Ukraine conflict. Furthermore, the results reveal some notable phenomena of media bias among multiple U.S. news outlets. While they exhibit diverse biases on different topics, some stereotypes are common, such as gender bias. This framework will be instrumental in helping people have a clearer insight into media bias and then fight against it to create a more fair and objective news environment. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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