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020 _a9783031019159
024 7 _a10.1007/978-3-031-01915-9
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aPN4784.F27
_b2019 EB
100 1 _aShu, Kai
_eautor
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9686288
245 1 0 _aDetecting Fake News on Social Media
_cby Kai Shu, Huan Liu
250 _a1st edition 2019
264 1 _aCham
_bSpringer International Publishing
_c2019
300 _a1 recurso en línea (XI, 121 páginas)
336 _atexto
_btxt
_2rdacontent
337 _aelectrónico
_bc
_2rdamedia
338 _arecurso electrónico
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
490 0 _aSynthesis Lectures on Data Mining and Knowledge Discovery
_x2151-0075
505 0 _aAcknowledgments -- Introduction -- What News Content Tells -- How Social Context Helps -- Challenging Problems of Fake News Detection -- Bibliography -- Authors' Biographies .
520 _aIn the past decade, social media has become increasingly popular for news consumption due to its easy access, fast dissemination, and low cost. However, social media also enables the wide propagation of "fake news," i.e., news with intentionally false information. Fake news on social media can have significant negative societal effects. Therefore, fake news detection on social media has recently become an emerging research area that is attracting tremendous attention. This book, from a data mining perspective, introduces the basic concepts and characteristics of fake news across disciplines, reviews representative fake news detection methods in a principled way, and illustrates challenging issues of fake news detection on social media. In particular, we discussed the value of news content and social context, and important extensions to handle early detection, weakly-supervised detection, and explainable detection. The concepts, algorithms, and methods described in this lecture can help harness the power of social media to build effective and intelligent fake news detection systems. This book is an accessible introduction to the study of detecting fake news on social media. It is an essential reading for students, researchers, and practitioners to understand, manage, and excel in this area. This book is supported by additional materials, including lecture slides, the complete set of figures, key references, datasets, tools used in this book, and the source code of representative algorithms. The readers are encouraged to visit the book website for the latest information: http://dmml.asu.edu/dfn/.
988 _aSynthesis Collection of Technology_2019
650 0 _9671085
_aNoticias falsas
650 7 _2embne
_9335445
_aMedios de comunicación social
_xObjetividad
700 1 _aLiu, Huan,
_eautor
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_949086
_d1958-
776 0 8 _iPrinted edition:
_z9783031001109
776 0 8 _iPrinted edition:
_z9783031007873
776 0 8 _iPrinted edition:
_z9783031030437
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01915-9
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
998 _b01/2023
_dz
_esc
_zSI