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Prediction and inference from social...

Agarwal, Nitin.

Prediction and inference from social networks and social media

Record Type:	Electronic resources : Monograph/item
Title/Author:	Prediction and inference from social networks and social mediaedited by Jalal Kawash, Nitin Agarwal, Tansel Ozyer.
other author:	Kawash, Jalal.
Published:	Cham :Springer International Publishing :2017.
Description:	ix, 225 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Social prediction.
Online resource:	http://dx.doi.org/10.1007/978-3-319-51049-1
ISBN:	9783319510491$q(electronic bk.)

Prediction and inference from social networks and social media
Prediction and inference from social networks and social media[electronic resource] /edited by Jalal Kawash, Nitin Agarwal, Tansel Ozyer. - Cham :Springer International Publishing :2017. - ix, 225 p. :ill., digital ;24 cm. - Lecture notes in social networks,2190-5428. - Lecture notes in social networks..

Chapter1. Having Fun?: Personalized Activity-based Mood Prediction in Social Media -- Chapter2. Automatic Medical Image Multilingual Indexation through a Medical Social Network -- Chapter3. The Significant Effect of Overlapping Community Structures in Signed Social Networks -- Chapter4. Extracting Relations Between Symptoms by Age-Frame Based Link Prediction -- Chapter5. Link Prediction by Network Analysis -- Chapter6. Structure-Based Features for Predicting the Quality of Articles in Wikipedia -- Chapter7. Predicting Collective Action from Micro-Blog Data -- Chapter8. Discovery of Structural and Temporal Patterns in MOOC Discussion Forums -- Chapter9. Diffusion Process in a Multi-Dimension Networks: Generating, Modelling and Simulation.

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.

ISBN: 9783319510491$q(electronic bk.)

Standard No.: 10.1007/978-3-319-51049-1doiSubjects--Topical Terms:

243023
Social prediction.

LC Class. No.: HM901

Dewey Class. No.: 303.49

Prediction and inference from social networks and social media
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505 0 $a Chapter1. Having Fun?: Personalized Activity-based Mood Prediction in Social Media -- Chapter2. Automatic Medical Image Multilingual Indexation through a Medical Social Network -- Chapter3. The Significant Effect of Overlapping Community Structures in Signed Social Networks -- Chapter4. Extracting Relations Between Symptoms by Age-Frame Based Link Prediction -- Chapter5. Link Prediction by Network Analysis -- Chapter6. Structure-Based Features for Predicting the Quality of Articles in Wikipedia -- Chapter7. Predicting Collective Action from Micro-Blog Data -- Chapter8. Discovery of Structural and Temporal Patterns in MOOC Discussion Forums -- Chapter9. Diffusion Process in a Multi-Dimension Networks: Generating, Modelling and Simulation.
520 $a 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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