000			03869cam a2200481Ii 4500
003			OCoLC
005			20240807160949.0
008			210504s2021 sz ob 000 0 eng d
020			_a9783030626969
020			_a3030626962
020			_z9783030626983
020			_z9783030626952
041			_aEnglish
049			_aMAIN
072		7	_aCOM030000 _2bisacsh
082	0	4	_a070.4 _222 _bDEE
100	1		_aP, Deepak,
100	1		_eauthor.
245	1	0	_aData science for fake news : _bsurveys and perspectives / _cDeepak P, Tanmoy Chakraboty, Cheng Long, Santhosh Kumar G.
260			_aCham : _bSpringer International Publishing Springer, _c2021.
300			_a1 online resource.
490	1		_aThe information retrieval series, _x1871-7500 ; _vvolume 42.
504			_aIncludes bibliographical references.
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.
506			_aAccess restricted to subscribing institutions.
520	8		_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	_aFake news.
650		0	_aJournalism
650		0	_aData mining.
650		0	_aJournalistic ethics.
650		0	_xData processing.
700	1		_aChakraborty, Tanmoy,
700	1		_aLong, Cheng,
700	1		_aG, Santhosh Kumar,
700	1		_eauthor.
700	1		_eauthor.
700	1		_eauthor.
830		0	_aInformation retrieval series ; _v42.
856	4	0	_uhttps://ezproxy.lib.gla.ac.uk/login?url=https://link.springer.com/10.1007/978-3-030-62696-9
856	4	0	_zConnect to e-book
907			_a.b37700224
942			_2ddc _cBOOKS
999			_c43342 _d43342