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020 _a9783031176937
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024 7 _a10.1007/978-3-031-17693-7
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082 0 4 _a006.35
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100 1 _aHamborg, Felix.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aRevealing Media Bias in News Articles
_h[electronic resource] :
_bNLP Techniques for Automated Frame Analysis /
_cby Felix Hamborg.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2023.
300 _aXIII, 238 p. 30 illus., 21 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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_2rda
505 0 _a1. Introduction -- 2. Media Bias Analysis -- 3. Person-Oriented Framing Analysis -- 4. Target Concept Analysis -- 5. Frame Analysis -- 6. Prototype -- 7. Conclusion. .
506 0 _aOpen Access
520 _aThis open access book presents an interdisciplinary approach to reveal biases in English news articles reporting on a given political event. The approach named person-oriented framing analysis identifies the coverage’s different perspectives on the event by assessing how articles portray the persons involved in the event. In contrast to prior automated approaches, the identified frames are more meaningful and substantially present in person-oriented news coverage. The book is structured in seven chapters: Chapter 1 presents a few of the severe problems caused by slanted news coverage and identifies the research gap that motivated the research described in this thesis. Chapter 2 discusses manual analysis concepts and exemplary studies from the social sciences and automated approaches, mostly from computer science and computational linguistics, to analyze and reveal media bias. This way, it identifies the strengths and weaknesses of current approaches for identifying and revealing media bias. Chapter 3 discusses the solution design space to address the identified research gap and introduces person-oriented framing analysis (PFA), a new approach to identify substantial frames and to reveal slanted news coverage. Chapters 4 and 5 detail target concept analysis and frame identification, the first and second component of PFA. Chapter 5 also introduces the first large-scale dataset and a novel model for target-dependent sentiment classification (TSC) in the news domain. Eventually, Chapter 6 introduces Newsalyze, a prototype system to reveal biases to non-expert news consumers by using the PFA approach. In the end, Chapter 7 summarizes the thesis and discusses the strengths and weaknesses of the thesis to derive ideas for future research on media bias. This book mainly targets researchers and graduate students from computer science, computational linguistics, political science, and further social sciences who want to get an overview of the relevant state of the art inthe other related disciplines and understand and tackle the issue of bias from a more effective, interdisciplinary viewpoint.
650 0 _aNatural language processing (Computer science).
650 0 _aMachine learning.
650 0 _aDigital media.
650 0 _aLinguistics.
650 0 _aPolitical science.
650 1 4 _aNatural Language Processing (NLP).
650 2 4 _aMachine Learning.
650 2 4 _aDigital and New Media.
650 2 4 _aLinguistics.
650 2 4 _aPolitical Science.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031176920
776 0 8 _iPrinted edition:
_z9783031176944
776 0 8 _iPrinted edition:
_z9783031176951
856 4 0 _uhttps://doi.org/10.1007/978-3-031-17693-7
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-SOB
999 _c37449
_d37449