Social Network Based Big Data Analysis and Applications

• Corporate Author: SpringerLink (Online service)
• Other Authors: Kaya, Mehmet (Editor, http://id.loc.gov/vocabulary/relators/edt), Kawash, Jalal (Editor, http://id.loc.gov/vocabulary/relators/edt), Khoury, Suheil (Editor, http://id.loc.gov/vocabulary/relators/edt), Day, Min-Yuh (Editor, http://id.loc.gov/vocabulary/relators/edt)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Edition: 1st ed. 2018.
• Series: Lecture Notes in Social Networks,
• Subjects: Social sciences-Data processing.; Social sciences-Computer programs.; Big data.; Data mining.; Physics.; Computational Social Sciences.; Big Data.; Data Mining and Knowledge Discovery.; Applications of Graph Theory and Complex Networks.

This book is a timely collection of chapters that present the state of the art within the analysis and application of big data. Working within the broader context of big data, this text focuses on the hot topics of social network modelling and analysis such as online dating recommendations, hiring p...

Table of Contents:

• Chapter1. Twitter as a Source for Time and Domain Dependent Sentiment Lexicons
• Chapter2. Hiding in Plain Sight: The Anatomy of Malicious Pages on Facebook
• Chapter3. Extraction and Analysis of Dynamic Conversational Networks from TV Series
• Chapter4. Diversity and Influence as Key Measures to Assess Candidates for Hiring or Promotion in Academia
• Chapter5. Timelines of Prostate Cancer Biomarkers
• Chapter6. Exploring the Role of Intrinsic Nodal Activation on the Spread of Influence in Complex Networks
• Chapter7. Influence and Extension of the Spiral of Silence in Social Networks: A Data-driven Approach
• Chapter8. Prepaid or Postpaid? That is the question.\\ Novel Methods of Subscription Type Prediction in Mobile Phone Services
• Chapter9. Dynamic Pattern Detection for Big Data Stream Analytics
• Chapter10. Community-based Recommendation for Cold-Start Problem: A Case Study of Reciprocal Online Dating Recommendation
• Chapter11. Combining Feature Extraction and Clustering for Better Face Recognition.