Overview
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.
Full Product Details
Author: Stan Matwin ,
Aristides Milios ,
Paweł Prałat ,
Amilcar Soares
Publisher: Springer International Publishing AG
Imprint: Springer International Publishing AG
Edition: 1st ed. 2023
Weight: 0.168kg
ISBN: 9783031336164
ISBN 10: 303133616
Pages: 90
Publication Date: 06 July 2023
Audience:
Professional and scholarly
,
Professional & Vocational
Format: Paperback
Publisher's Status: Active
Availability: Manufactured on demand
We will order this item for you from a manufactured on demand supplier.
Author Information
Stan Matwin is a Canada Research Chair at the Faculty of Computer Science, Dalhousie University, and the Director of the Institute for Big Data Analytics. He is also Emeritus Distinguished Professor of Computer Science at the University of Ottawa, and a Professor in the Institute of Computer Science of the Polish Academy of Sciences.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.
