000			03786nam a22003735i 4500
001			394200
003			ES-MaUEC
005			20230102123106.0
007			cr nn 008mamaa
008			221018s2022 sz | s |||| 0|eng d
020			_a9783031082429
024	7		_a10.1007/978-3-031-08242-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
245	1	0	_aSocial Media Analysis for Event Detection _cedited by Tansel Özyer
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (VI, 229 páginas) _b1 illus
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aLecture Notes in Social Networks _x2190-5436
505	0		_aChapter 1. A network-based approach to understanding international cooperation in environmental protection (Andreea Nita) -- Chapter 2. Critical Mass and Data Integrity Diagnostics of a Twitter Social Contagion Monitor (Amruta Deshpande) -- Chapter 3. TenFor: Tool to Mine Interesting Events from Security Forums Leveraging Tensor Decomposition (Risul Islam) -- Chapter 4. Profile Fusion in Social Networks: A Data-Driven Approach -Youcef Benkhedda) -- Chapter 5. RISECURE: Metro Transit Disruptions Detection Using Social Media Mining And Graph Convolution (Omer Zulqar) -- Chapter 6. Local Taxonomy Construction: An Information Retrieval Approach Using Representation Learning (Mayank Kejriwal) -- Chapter 7. The evolution of online sentiments across Italy during first and second wave of the COVID-19 pandemic (Francesco Scotti) -- Chapter 8. Inferring Degree of Localization and Popularity of Twitter Topics and Persons using Temporal Features (Aleksey Panasyuk) -- Chapter 9. Covid-19 and Vaccine Tweet Analysis (Eren Alp).
520			_aThis book includes chapters which discuss effective and efficient approaches in dealing with various aspects of social media analysis by using machine learning techniques from clustering to deep learning. A variety of theoretical aspects, application domains and case studies are covered to highlight how it is affordable to maximize the benefit of various applications from postings on social media platforms. Social media platforms have significantly influenced and reshaped various social aspects. They have set new means of communication and interaction between people, turning the whole world into a small village where people with internet connect can easily communicate without feeling any barriers. This has attracted the attention of researchers who have developed techniques and tools capable of studying various aspects of posts on social media platforms with main concentration on Twitter. This book addresses challenging applications in this dynamic domain where it is not possible to continue applying conventional techniques in studying social media postings. The content of this book helps the reader in developing own perspective about how to benefit from machine learning techniques in dealing with social media postings and how social media postings may directly influence various applications.
700	1		_aÖzyer, Tansel _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783031082412
776	0	8	_iPrinted edition: _z9783031082436
776	0	8	_iPrinted edition: _z9783031082443
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-08242-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aSpringer_Computer_2022
999			_c394200 _d394200