Generative Methods for Social Media Analysis
Language: English
Published by Springer, Berlin|Springer Nature Switzerland|Springer, 2023
- Softcover
- New
Condition: New
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Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This 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: dyn.
Seller Inventory # 859348071
- Title
- Generative Methods for Social Media Analysis
- Author
- Matwin, Stan|Milios, Aristides|Pralat, Pawel|Soares, Amilcar|Théberge, François
- Publisher
- Springer, Berlin|Springer Nature Switzerland|Springer
- Publication year
- 2023
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 303133616X
- ISBN 13
- 9783031336164
- Series
- Book 35 of 103: SpringerBriefs in Computer Science
This 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.
"Synopsis" may belong to another edition of this title.
About the Author
Aristides Milios is a Researcher at McGill University in Montreal and passionate about the intersection between Natural Language Processing (NLP) and Machine Learning; specifically, about the latent few-shot learning abilities of large language models.
Paweł Prałat is a Professor at the Department of Mathematics at Toronto Metropolitan University (formerly Ryerson University). His research is focused on modelling and mining complex networks.
Amilcar Soares is an Assistant Professor in the Department of Computer Science at Memorial University of Newfoundland. His research interests include spatiotemporal data enrichment, segmentation, classification, clustering, and visualization.
François Théberge is a mathematician with the Tutte Institute for Mathematics and Computing in Ottawa. His research focuses on data science for relational data.
"About the title" may belong to another edition of this title.
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