Overview
- Illustrates the usage of machine learning techniques for social media analysis
- Contains case studies describing how various domains may benefit from social media analysis
- Includes practical test results from synthetic and real data
Part of the book series: Lecture Notes in Social Networks (LNSN)
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Table of contents (9 chapters)
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Front Matter
Keywords
About this book
This 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.
Editors and Affiliations
About the editor
Bibliographic Information
Book Title: Social Media Analysis for Event Detection
Editors: Tansel Özyer
Series Title: Lecture Notes in Social Networks
DOI: https://doi.org/10.1007/978-3-031-08242-9
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-031-08241-2Published: 20 October 2022
Softcover ISBN: 978-3-031-08244-3Published: 21 October 2023
eBook ISBN: 978-3-031-08242-9Published: 18 October 2022
Series ISSN: 2190-5428
Series E-ISSN: 2190-5436
Edition Number: 1
Number of Pages: VI, 229
Number of Illustrations: 1 b/w illustrations
Topics: Data Structures and Information Theory, Artificial Intelligence, Social Media, Natural Language Processing (NLP), Graph Theory, Machine Learning