Overview
- Demonstrates the power of social media data analysis
- Features behavior analysis and understanding as important for a variety of applications
- Forms a good source for practitioners and researchers, including instructors and students
Part of the book series: Lecture Notes in Social Networks (LNSN)
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Table of contents (12 chapters)
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Front Matter
Keywords
About this book
This book focusses on recommendation, behavior, and anomaly, among of social media analysis. First, recommendation is vital for a variety of applications to narrow down the search space and to better guide people towards educated and personalized alternatives. In this context, the book covers supporting students, food venue, friend and paper recommendation to demonstrate the power of social media data analysis. Secondly, this book treats behavior analysis and understanding as important for a variety of applications, including inspiring behavior from discussion platforms, determining user choices, detecting following patterns, crowd behavior modeling for emergency evacuation, tracking community structure, etc. Third, fraud and anomaly detection have been well tackled based on social media analysis. This has is illustrated in this book by identifying anomalous nodes in a network, chasing undetected fraud processes, discovering hidden knowledge, detecting clickbait, etc. With this widecoverage, the book forms a good source for practitioners and researchers, including instructors and students.
Editors and Affiliations
Bibliographic Information
Book Title: Putting Social Media and Networking Data in Practice for Education, Planning, Prediction and Recommendation
Editors: Mehmet Kaya, Şuayip Birinci, Jalal Kawash, Reda Alhajj
Series Title: Lecture Notes in Social Networks
DOI: https://doi.org/10.1007/978-3-030-33698-1
Publisher: Springer Cham
eBook Packages: Physics and Astronomy, Physics and Astronomy (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-33697-4Published: 28 December 2019
Softcover ISBN: 978-3-030-33700-1Published: 28 December 2020
eBook ISBN: 978-3-030-33698-1Published: 27 December 2019
Series ISSN: 2190-5428
Series E-ISSN: 2190-5436
Edition Number: 1
Number of Pages: XIII, 237
Number of Illustrations: 17 b/w illustrations, 51 illustrations in colour
Topics: Data-driven Science, Modeling and Theory Building, Computational Social Sciences, Big Data/Analytics, Computer Appl. in Social and Behavioral Sciences