000			02104cam a2200277 i 4500
020			_a9783031336164
020			_a9783031336164
082	0	4	_aP91 .S73 2022 _b2
100	1		_aMatwin, Stan,
245	1	0	_aGenerative methods for social media analysis / _cStan Matwin, Aristides Milios, Paweł Prałat Amilcar Soares, Fraṅois Tȟberge
264		1	_aCham, Switzerland _bSpringer, _c2023
300			_avii,90 pages _billustration
490	1		_aSpringerBriefs in Computer Science Series
520			_aThis book provides a broad overview of the state of the art of the research in generative methods for the analysis of social media data. It especially includes two important aspects that currently gain importance in mining and modelling social media: dynamics and networks. The book is divided into five chapters and provides an extensive bibliography consisting of more than 250 papers. After a quick introduction and survey of the book in the first chapter, chapter 2 is devoted to the discussion of data models and ontologies for social network analysis. Next, chapter 3 deals with text generation and generative text models and the dangers they pose to social media and society at large. Chapter 4 then focuses on topic modelling and sentiment analysis in the context of social networks. Finally, Chapter 5 presents graph theory tools and approaches to mine and model social networks. Throughout the book, open problems, highlighting potential future directions, are clearly identified. The book aims at researchers and graduate students in social media analysis, information retrieval, and machine learning applications
650		0	_aSocial media
650		0	_aData mining.
650		0	_aSocial media
700	1		_aMilios, Aristides,
700	1		_aPrałat, Paweł,
700	1		_aSoares, Amilcar,
700	1		_aThéberge, Françoisv,
856	4	0	_uhttps://rave.ohiolink.edu/ebooks/ebc2/9783031336171
856	4	0	_uhttps://link.springer.com/10.1007/978-3-031-33617-1
856	4	0	_uhttps://go.ohiolink.edu/goto?url=https://link.springer.com/10.1007/978-3-031-33617-1
942			_2lcc _cBK
999			_c13874 _d13874