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008			230126s2022 sz | s |||| 0|eng d
020			_a9783030900878
024	7		_a10.1007/978-3-030-90087-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ342 _b2022 EB
245	0	0	_aCombating Fake News with Computational Intelligence Techniques _cedited by Mohamed Lahby, Al-Sakib Khan Pathan, Yassine Maleh, Wael Mohamed Shaher Yafooz
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XIII, 435 páginas) _b156 ilustraciones, 135 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aStudies in Computational Intelligence _x1860-9503 _v1001
505	0		_aPart I: State-of-the-art -- Online Fake News Detection Using Machine Learning Techniques: A Systematic Mapping Study -- Using Artificial Intelligence against the Phenomenon of Fake News: a Systematic Literature Review -- Fake news detection in internet using deep learning: A review -- Part II: Machine Learning Techniques and Fake News -- Early Detection of Fake News from Social Media Networks using Computational Intelligence Approaches -- Fandet Semantic Model: An OWL Ontology for Context-Based Fake News Detection on Social Media -- Fake News Detection using Machine Learning and Natural Language Processing -- Fake News Detection using Ensemble Learning and Machine Learning Algorithms -- Evaluation of Machine Learning Methods for Fake News Detection -- Credibility and Reliability News Evaluation Based on Artificial Intelligent Service with Feature Segmentation Searching and Dynamic Clustering -- Deep Learning with Self-Attention Mechanism for Fake News Detection -- Modeling and solving the fake news detection scheduling problem -- Part III: Case Studies and Frameworks -- The multiplier effect on the dissemination of false speeches on social networks: Experiment during the silly season in Spain -- Detecting News Influence in a Country: One Step Forward Towards Understanding Fake News -- Factors Affecting the Intention of Using Fintech Services in the Context of Combating of Fake News -- Crowd Sourcing and Blockchain-based Incentive Mechanism to Combat Fake News -- Framework for Fake News Classification using Vectorization and Machine Learning -- Fact Checking: An Automatic end to end Fact Checking System -- Part IV: Fake news and Covid-19 pandemic -- False Information in a Post Covid-19 World -- Applying Fuzzy Logic and Neural Network in Sentiment Analysis for fake news detection: Case of Covid-19 -- Analyzing Deep Learning Optimizers for COVID-19 Fake News Detection -- Detecting Fake News On COVID-19 Vaccine from YouTube Videos Using Advanced Machine Learning Approaches.
520			_aThis book presents the latest cutting-edge research, theoretical methods, and novel applications in the field of computational intelligence techniques and methods for combating fake news. Fake news is everywhere. Despite the efforts of major social network players such as Facebook and Twitter to fight disinformation, miracle cures and conspiracy theories continue to rain down on the net. Artificial intelligence can be a bulwark against the diversity of fake news on the Internet and social networks. This book discusses new models, practical solutions, and technological advances related to detecting and analyzing fake news based on computational intelligence models and techniques, to help decision-makers, managers, professionals, and researchers design new paradigms considering the unique opportunities associated with computational intelligence techniques. Further, the book helps readers understand computational intelligence techniques combating fake news in a systematic and straightforward way.
988			_aSpringer_Robotics_2022
650		0	_9671085 _aNoticias falsas
650		7	_2embne _aInteligencia artificial _9413115
776	0	8	_iPrinted edition: _z9783030900861
776	0	8	_iPrinted edition: _z9783030900885
776	0	8	_iPrinted edition: _z9783030900892
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi-org.ezproxy.universidadeuropea.es/10.1007/978-3-030-90087-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eIG _zSI