Prediction and inference from social networks and social media
Title By: Kawash, Jalal [Edited by] | Agarwal, Nitin [Edited by] | Ozyer, Tansel [Edited by]
Material type: BookSeries: Lecture Notes in Social Networks.Publisher: Cham : Springer International Publishing, c2017.Description: ix, 225 p. : ill. ; 25 cm.ISBN: 9783319510491; 9783319510484Subject(s): Computer science | Data mining | User interfaces (Computer systems) | Computers and civilizationDDC classification: 006.312 PR ED Online resources: Location MapItem type | Home library | Call number | Status | Date due | Barcode | Item holds |
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REGULAR | University of Wollongong in Dubai Main Collection | 006.312 PR ED (Browse shelf) | Available | T0056835 |
, Shelving location: Main Collection Close shelf browser
006.312 OL DA Data mining models | 006.312 PA HA Harness the power of Big Data : | 006.312 PR DA Data science for business | 006.312 PR ED Prediction and inference from social networks and social media | 006.312 RE CO Recommendation and search in social networks | 006.312 RO DA Data mining with decision trees : | 006.312 RO DA Data science and analytics with Python |
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.