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Social Media Mining

Book Description

The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts, principles and methods in various scenarios of social media mining.

Table of Contents

  1. Cover
  2. Half title
  3. Title
  4. Copyright
  5. Dedication
  6. Table of Contents
  7. Preface
  8. Acknowledgments
  9. Chapter 1 Introduction
    1. 1.1 What Is Social Media Mining
    2. 1.2 New Challenges for Mining
    3. 1.3 Book Overview and Reader’s Guide
    4. 1.4 Summary
    5. 1.5 Bibliographic Notes
    6. 1.6 Exercises
  10. Part I Essentials
    1. Chapter 2 Graph Essentials
      1. 2.1 Graph Basics
      2. 2.2 Graph Representation
      3. 2.3 Types of Graphs
      4. 2.4 Connectivity in Graphs
      5. 2.5 Special Graphs
      6. 2.6 Graph Algorithms
      7. 2.7 Summary
      8. 2.8 Bibliographic Notes
      9. 2.9 Exercises
    2. Chapter 3 Network Measures
      1. 3.1 Centrality
      2. 3.2 Transitivity and Reciprocity
      3. 3.3 Balance and Status
      4. 3.4 Similarity
      5. 3.5 Summary
      6. 3.6 Bibliographic Notes
      7. 3.7 Exercises
    3. Chapter 4 Network Models
      1. 4.1 Properties of Real-World Networks
      2. 4.2 Random Graphs
      3. 4.3 Small-World Model
      4. 4.4 Preferential Attachment Model
      5. 4.5 Summary
      6. 4.6 Bibliographic Notes
      7. 4.7 Exercises
    4. Chapter 5 Data Mining Essentials
      1. 5.1 Data
      2. 5.2 Data Preprocessing
      3. 5.3 Data Mining Algorithms
      4. 5.4 Supervised Learning
      5. 5.5 Unsupervised Learning
      6. 5.6 Summary
      7. 5.7 Bibliographic Notes
      8. 5.8 Exercises
  11. Part II Communities and Interactions
    1. Chapter 6 Community Analysis
      1. 6.1 Community Detection
      2. 6.2 Community Evolution
      3. 6.3 Community Evaluation
      4. 6.4 Summary
      5. 6.5 Bibliographic Notes
      6. 6.6 Exercises
    2. Chapter 7 Information Diffusion in Social Media
      1. 7.1 Herd Behavior
      2. 7.2 Information Cascades
      3. 7.3 Diffusion of Innovations
      4. 7.4 Epidemics
      5. 7.5 Summary
      6. 7.6 Bibliographic Notes
      7. 7.7 Exercises
  12. Part III Applications
    1. Chapter 8 Influence and Homophily
      1. 8.1 Measuring Assortativity
      2. 8.2 Influence
      3. 8.3 Homophily
      4. 8.4 Distinguishing Influence and Homophily
      5. 8.5 Summary
      6. 8.6 Bibliographic Notes
      7. 8.7 Exercises
    2. Chapter 9 Recommendation in Social Media
      1. 9.1 Challenges
      2. 9.2 Classical Recommendation Algorithms
      3. 9.3 Recommendation Using Social Context
      4. 9.4 Evaluating Recommendations
      5. 9.5 Summary
      6. 9.6 Bibliographic Notes
      7. 9.7 Exercises
    3. Chapter 10 Behavior Analytics
      1. 10.1 Individual Behavior
      2. 10.2 Collective Behavior
      3. 10.3 Summary
      4. 10.4 Bibliographic Notes
      5. 10.5 Exercises
  13. Notes
  14. Bibliography
  15. Index