Prediction and Inference from Social Networks and Social Media
Produktnummer:
18f0fccc5238ba4ab180e9efaa2d2b9bb9
| Themengebiete: | activity-based mood prediction link prediction modeling multi-dimensional networks overlapping community structures predicting collective action ranking social content trend prediction |
|---|---|
| Veröffentlichungsdatum: | 18.03.2017 |
| EAN: | 9783319510484 |
| Sprache: | Englisch |
| Seitenzahl: | 225 |
| Produktart: | Gebunden |
| Herausgeber: | Agarwal, Nitin Kawash, Jalal Özyer, Tansel |
| Verlag: | Springer International Publishing |
Produktinformationen "Prediction and Inference from Social Networks and Social Media"
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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