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Prediction and inference from social networks and social media edited by Jalal Kawash, Nitin Agarwal, Tansel Ozyer

Contributor(s): Material type: TextTextSeries: Lecture Notes in Social NetworksPublication details: Cham : Springer International Publishing, c2017.Description: ix, 225 p. : ill. ; 25 cmISBN:
  • 9783319510491
  • 9783319510484
Subject(s): DDC classification:
  • 006.312 PR ED
Online resources: Summary: 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.
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Item type Current library Call number Status Date due Barcode
REGULAR University of Wollongong in Dubai Main Collection 006.312 PR ED (Browse shelf(Opens below)) Available T0056835

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.

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