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020 _a9783030626969
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024 7 _a10.1007/978-3-030-62696-9
_2doi
050 4 _aQA75.5-76.95
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100 1 _aP, Deepak.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aData Science for Fake News
_h[electronic resource] :
_bSurveys and Perspectives /
_cby Deepak P, Tanmoy Chakraborty, Cheng Long, Santhosh Kumar G.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXIV, 302 p. 70 illus., 17 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
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347 _atext file
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490 1 _aThe Information Retrieval Series,
_x2730-6836 ;
_v42
505 0 _aA Multifaceted Approach to Fake News -- Part I: Survey -- On Unsupervised Methods for Fake News Detection -- Multi-modal Fake News Detection -- Deep Learning for Fake News Detection -- Dynamics of Fake News Diffusion -- Neural Language Models for (Fake?) News Generation -- Fact Checking on Knowledge Graphs -- Graph Mining Meets Fake News Detection -- Part II: Perspectives -- Fake News in Health and Medicine -- Ethical Considerations in Data-Driven Fake News Detection -- A Political Science Perspective on Fake News -- A Political Science Perspective on Fake News -- Fake News and Social Processes: A Short Review -- Misinformation and the Indian Election: Case Study -- STS, Data Science, and Fake News: Questions and Challenges -- Linguistic Approaches to Fake News Detection.
520 _aThis book provides an overview of fake news detection, both through a variety of tutorial-style survey articles that capture advancements in the field from various facets and in a somewhat unique direction through expert perspectives from various disciplines. The approach is based on the idea that advancing the frontier on data science approaches for fake news is an interdisciplinary effort, and that perspectives from domain experts are crucial to shape the next generation of methods and tools. The fake news challenge cuts across a number of data science subfields such as graph analytics, mining of spatio-temporal data, information retrieval, natural language processing, computer vision and image processing, to name a few. This book will present a number of tutorial-style surveys that summarize a range of recent work in the field. In a unique feature, this book includes perspective notes from experts in disciplines such as linguistics, anthropology, medicine and politics that will help to shape the next generation of data science research in fake news. The main target groups of this book are academic and industrial researchers working in the area of data science, and with interests in devising and applying data science technologies for fake news detection. For young researchers such as PhD students, a review of data science work on fake news is provided, equipping them with enough know-how to start engaging in research within the area. For experienced researchers, the detailed descriptions of approaches will enable them to take seasoned choices in identifying promising directions for future research.
650 0 _aInformation storage and retrieval systems.
650 0 _aData protection.
650 0 _aData mining.
650 0 _aCommunication.
650 1 4 _aInformation Storage and Retrieval.
650 2 4 _aData and Information Security.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aMedia and Communication.
700 1 _aChakraborty, Tanmoy.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aLong, Cheng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aG, Santhosh Kumar.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030626952
776 0 8 _iPrinted edition:
_z9783030626976
776 0 8 _iPrinted edition:
_z9783030626983
830 0 _aThe Information Retrieval Series,
_x2730-6836 ;
_v42
856 4 0 _uhttps://doi.org/10.1007/978-3-030-62696-9
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c177387
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