Editors:
- Demonstrates new mining techniques and applications for social networking within the fields of prediction and inference
- Proposes a wide variety of social network research topics
- Covers a wide variety of case studies and state-of-the-art analysis tools for Facebook and Twitter
- Includes supplementary material: sn.pub/extras
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
About this book
This book addresses the challenges of social network and social media analysis in terms of prediction and inference. The chapters collected here tackle these issues by proposing new analysis methods and by examining mining methods for the vast amount of social content produced. Social Networks (SNs) have become an integral part of our lives; they are used for leisure, business, government, medical, educational purposes and have attracted billions of users. The challenges that stem from this wide adoption of SNs are vast. These include generating realistic social network topologies, awareness of user activities, topic and trend generation, estimation of user attributes from their social content, and behavior detection. This text has applications to widely used platforms such as Twitter and Facebook and appeals to students, researchers, and professionals in the field.
Editors and Affiliations
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Department of Computer Science, University of Calgary, Calgary, Canada
Jalal Kawash
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Information Science Department, University of Arkansas at Little Rock, Little Rock, USA
Nitin Agarwal
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Department of Computer Engineering, TOBB University, Ankara, Turkey
Tansel Özyer
Bibliographic Information
Book Title: Prediction and Inference from Social Networks and Social Media
Editors: Jalal Kawash, Nitin Agarwal, Tansel Özyer
Series Title: Lecture Notes in Social Networks
DOI: https://doi.org/10.1007/978-3-319-51049-1
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-51048-4Published: 18 March 2017
Softcover ISBN: 978-3-319-84553-1Published: 18 July 2018
eBook ISBN: 978-3-319-51049-1Published: 16 March 2017
Series ISSN: 2190-5428
Series E-ISSN: 2190-5436
Edition Number: 1
Number of Pages: IX, 225
Number of Illustrations: 28 b/w illustrations, 54 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Applications of Graph Theory and Complex Networks, Computers and Society, User Interfaces and Human Computer Interaction