Prediction and inference from social networks and social media

• 其他作者：
  • Kawash, Jalal, editor.
  • Agarwal, Nitin, editor.
  • Ozyer, Tansel, editor.
  • SpringerLink (Online service)
• 其他題名：
  • Lecture notes in social networks.
• 出版： Cham : Springer International Publishing :Imprint: Springer
• 叢書名： Lecture notes in social networks,
• 主題： Social prediction. , Social networks. , Social media. , Computer Science. , Data Mining and Knowledge Discovery. , Applications of Graph Theory and Complex Networks. , Computers and Society. , User Interfaces and Human Computer Interaction.
• ISBN： 9783319510491 (electronic bk.) 、 9783319510484 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: 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.
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• 摘要註: 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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