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001471555 001__ 1471555
001471555 003__ OCoLC
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001471555 019__ $$a1389606344$$a1390096693$$a1393280347
001471555 020__ $$a9783031336171
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001471555 020__ $$z9783031336164
001471555 0247_ $$a10.1007/978-3-031-33617-1$$2doi
001471555 035__ $$aSP(OCoLC)1389612065
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001471555 050_4 $$aQA76.9.Q36
001471555 08204 $$a302.23102856312
001471555 1001_ $$aMatwin, Stan,$$eauthor.
001471555 24510 $$aGenerative methods for social media analysis /$$cStan Matwin, Aristides Milios, Paweł Prałat Amilcar Soares, François Théberge.
001471555 264_1 $$aCham :$$bSpringer,$$c2023.
001471555 300__ $$a1 online resource (92 p.).
001471555 336__ $$atext$$btxt$$2rdacontent
001471555 337__ $$acomputer$$bc$$2rdamedia
001471555 338__ $$aonline resource$$bcr$$2rdacarrier
001471555 4901_ $$aSpringerBriefs in Computer Science Series
001471555 500__ $$aDescription based upon print version of record.
001471555 504__ $$aIncludes bibliographical references.
001471555 5050_ $$aIntro -- Acknowledgments -- Contents -- 1 Introduction -- 2 Ontologies and Data Models for Cross-platform Social Media Data -- 2.1 Data Models for Social Media Data Analysis -- Homophily Analysis -- Social Identity Linkage -- Personality Analysis -- 2.2 Ontologies for Social Media Data -- Ontologies for Sentiment Analysis -- Ontologies for Situational Awareness -- 2.3 Potential Future Research Topics -- Metadata -- Federated Learning -- 3 Methods for Text Generation in NLP -- 3.1 Introduction -- 3.2 Past Approaches -- 3.3 GANs in NLP -- Reinforcement learning strategies -- Operating on continuous representations instead of discrete symbols -- Gumbel-softmax -- 3.4 Large Neural Language Models (LNLMs or LLMs) -- The Transformer and BERT -- BERT variants -- Introduction to GPT-3 -- 3.5 Dangers of E ective Generative LLMs -- Marginalized Group and Gender Bias -- Generation of Hateful Content -- De-biasing Approaches -- Environmental and Financial Impacts -- Identifying Information Extraction Attacks -- Simpler Approaches -- Potential Research Direction # 1 (Large Neural Language Models) -- 3.6 Detecting Generated Text -- Overview -- Detection of Machine-Generated Text -- The Issue with Simple Detection -- Detection of Fake News Content -- Issues of Comparison and Dataset Standardization -- Content-based Approaches -- Social-response-based Approaches -- Hybrid Approaches -- Graph-based Approaches -- Multimodal Approaches: Incorporating Visual Information -- Potential Research Direction # 2 (Fake News Detection) -- 4 Topic and Sentiment Modelling for Social Media -- 4.1 Introduction -- 4.2 Introduction to Topic Modelling -- 4.3 Overview of Classical Approaches to Topic Modelling -- LDA -- 4.4 Neural Topic Modelling -- Variational Topic Modelling -- LDA2Vec -- Top2Vec -- Use of Pre-trained Embeddings for Neural Topic Modelling.
001471555 5058_ $$aNeural Topic Modelling for Social Media -- Potential Research Direction # 3 (Extending NTMs) -- 4.5 Sentiment Analysis -- Sentiment Analysis and Stance Detection Standardized Datasets -- Traditional Supervised Sentiment Analysis -- Multimodal Sentiment Analysis -- Potential Research Direction # 4 (Textual Sentiment Analysis over Time) -- Aspect-based Sentiment Analysis -- ASBA in a Uni ed Framework -- Potential Research Direction # 5 (Aspect-based Multimodal Sentiment Analysis) -- 5 Mining and Modelling Complex Networks -- 5.1 Node Embeddings -- Hyperbolic Spaces -- Signed Networks -- Potential Research Direction # 6 (Embedding Sequences of Graphs) -- Potential Research Direction # 7 (Multi-Layered Graphs) -- 5.2 Evaluating Node Embeddings -- Potential Research Direction # 8 (Selecting an Appropriate Embedding for a Given Task at Hand-Supervised vs. Unsupervised Approach) -- 5.3 Community Detection -- Potential Research Direction # 9 (More General Community Detection and Using Several Sources of Information) -- 5.4 Hypergraphs -- Potential Research Direction # 10 (Hypergraph Modularity Function) -- 5.5 Understanding the Dynamics of Networks -- Human-bot Interaction and Spread of Misinformation -- Social Bursts in Collective Attention -- Social Learning (Segregation, Polarization) -- Potential Research Direction # 11 (Tools Based on the Null-models) -- 5.6 Generating Synthetic Networks -- Potential Research Direction # 12 (Generating Synthetic Higher-order Structures) -- 6 Conclusions -- References.
001471555 506__ $$aAccess limited to authorized users.
001471555 520__ $$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.
001471555 650_0 $$aSocial media$$xData processing.
001471555 650_0 $$aData mining.
001471555 650_0 $$aSocial media$$xResearch$$xMethodology.
001471555 655_0 $$aElectronic books.
001471555 7001_ $$aMilios, Aristides,$$eauthor.
001471555 7001_ $$aPrałat, Paweł,$$eauthor.
001471555 7001_ $$aSoares, Amilcar,$$eauthor.
001471555 7001_ $$aThéberge, Françoisv,$$eauthor.
001471555 77608 $$iPrint version:$$aMatwin, Stan$$tGenerative Methods for Social Media Analysis$$dCham : Springer International Publishing AG,c2023$$z9783031336164
001471555 830_0 $$aSpringerBriefs in computer science.
001471555 852__ $$bebk
001471555 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-33617-1$$zOnline Access$$91397441.1
001471555 909CO $$ooai:library.usi.edu:1471555$$pGLOBAL_SET
001471555 980__ $$aBIB
001471555 980__ $$aEBOOK
001471555 982__ $$aEbook
001471555 983__ $$aOnline
001471555 994__ $$a92$$bISE