000			04150nam a22004455i 4500
999			_c361483 _d361483 _x1
001			361483
003			ES-MaUEC
005			20230102121402.0
006			a||||fo|||| 00| 0
007			cr nn nnnaamaa
008			210429s2021 sz | s |||| 0|eng d
020			_a9783030626969
024	7		_a10.1007/978-3-030-62696-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aPN4784 .F27 _b2021 EB
100	1		_aP., Deepak _eautor _9681556
245	1	0	_aData Science for Fake News : _bSurveys and Perspectives _cby Deepak P, Tanmoy Chakraborty, Cheng Long, Santhosh Kumar G.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publising _c2021
300			_a1 recurso en línea (XIV, 302 páginas) _b70 ilustraciones, 17 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aThe Information Retrieval Series _x2730-6836 _v42
490	0		_aComputer Science (SpringerNature-11645)
490	0		_aComputer Science (R0) (SpringerNature-43710)
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	3		_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.
988			_aSpringer_Computer_2021
650		0	_aNoticias falsas _9671085
650		7	_2embne _9162648 _aData mining
650		7	_2embne _9413124 _aÉtica periodística
700	1		_aChakraborty, Tanmoy _eautor _9681557 _d1988-
700	1		_aLong, Cheng _eautor _9681558
700	1		_aG., Santhosh Kumar _eautor _9681559
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-62696-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2022 _dz _eu _zSI