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020 _a9783031082429
_9978-3-031-08242-9
024 7 _a10.1007/978-3-031-08242-9
_2doi
050 4 _aQ336
072 7 _aUN
_2bicssc
072 7 _aCOM021000
_2bisacsh
072 7 _aUN
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082 0 4 _a005.7
_223
245 1 0 _aSocial Media Analysis for Event Detection
_h[electronic resource] /
_cedited by Tansel Özyer.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aVI, 229 p. 1 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aArtificial intelligence
_xData processing.
650 0 _aSocial media.
650 0 _aNatural language processing (Computer science).
650 0 _aGraph theory.
650 0 _aMachine learning.
650 1 4 _aData Science.
650 2 4 _aSocial Media.
650 2 4 _aNatural Language Processing (NLP).
650 2 4 _aGraph Theory.
650 2 4 _aMachine Learning.
700 1 _aÖzyer, Tansel.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031082412
776 0 8 _iPrinted edition:
_z9783031082436
776 0 8 _iPrinted edition:
_z9783031082443
830 0 _aLecture Notes in Social Networks,
_x2190-5436
856 4 0 _uhttps://doi.org/10.1007/978-3-031-08242-9
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c174026
_d174026