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Social Sensing

Book Description

Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individuals. The title offers theoretical foundations to support emerging data-driven cyber-physical applications and touches on key issues such as privacy. The authors present solutions based on recent research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion.



  • Offers a unique interdisciplinary perspective bridging social networks, big data, cyber-physical systems, and reliability
  • Presents novel theoretical foundations for assured social sensing and modeling humans as sensors
  • Includes case studies and application examples based on real data sets
  • Supplemental material includes sample datasets and fact-finding software that implements the main algorithms described in the book

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Dedication
  6. Acknowledgments
  7. Authors
  8. Foreword
  9. Preface
  10. Chapter 1: A new information age
    1. Abstract
    2. 1.1 Overview
    3. 1.2 Challenges
    4. 1.3 State of the Art
    5. 1.4 Organization
  11. Chapter 2: Social sensing trends and applications
    1. Abstract
    2. 2.1 Information Sharing: The Paradigm Shift
    3. 2.2 An Application Taxonomy
    4. 2.3 Early Research
    5. 2.4 The Present Time
    6. 2.5 A Note on Privacy
  12. Chapter 3: Mathematical foundations of social sensing: An introductory tutorial
    1. Abstract
    2. 3.1 A Multidisciplinary Background
    3. 3.2 Basics of Generic Networks
    4. 3.3 Basics of Bayesian Analysis
    5. 3.4 Basics of Maximum Likelihood Estimation
    6. 3.5 Basics of Expectation Maximization
    7. 3.6 Basics of Confidence Intervals
    8. 3.7 Putting It All Together
  13. Chapter 4: Fact-finding in information networks
    1. Abstract
    2. 4.1 Facts, Fact-Finders, and the Existence of Ground Truth
    3. 4.2 Overview of Fact-Finders in Information Networks
    4. 4.3 A Bayesian Interpretation of Basic Fact-Finding
    5. 4.4 The Iterative Algorithm
    6. 4.5 Examples and Results
    7. 4.6 Discussion
    8. Appendix
  14. Chapter 5: Social Sensing: A maximum likelihood estimation approach
    1. Abstract
    2. 5.1 The Social Sensing Problem
    3. 5.2 Expectation Maximization
    4. 5.3 The EM Fact-Finding Algorithm
    5. 5.4 Examples and Results
    6. 5.5 Discussion
  15. Chapter 6: Confidence bounds in social sensing
    1. Abstract
    2. 6.1 The Reliability Assurance Problem
    3. 6.2 Actual Cramer-Rao Lower Bound
    4. 6.3 Asymptotic Cramer-Rao Lower Bound
    5. 6.4 Confidence Interval Derivation
    6. 6.5 Examples and Results
    7. 6.6 Discussion
    8. Appendix
  16. Chapter 7: Resolving conflicting observations and non-binary claims
    1. Abstract
    2. 7.1 Handling Conflicting Binary Observations
    3. 7.2 Handling Non-Binary Claims
    4. 7.3 Performance Evaluation
    5. 7.4 Discussion
    6. Appendix
  17. Chapter 8: Understanding the social network
    1. Abstract
    2. 8.1 Information Propagation Cascades
    3. 8.2 A Binary Model of Human Sensing
    4. 8.3 Inferring the Social Network
    5. 8.4 A Social-Aware Algorithm
    6. 8.5 Evaluation
    7. 8.6 Discussion and Limitations
  18. Chapter 9: Understanding physical dependencies: Social meets cyber-physical
    1. Abstract
    2. 9.1 Correlations in the Physical World
    3. 9.2 Accounting for the Opportunity to Observe
    4. 9.3 Accounting for Physical Dependencies
    5. 9.4 Real-World Case Studies
    6. 9.5 Discussion
    7. Appendix
  19. Chapter 10: Recursive fact-finding
    1. Abstract
    2. 10.1 Real Time Social Sensing
    3. 10.2 A Streaming Truth Estimation Model
    4. 10.3 Dynamics and the Recursive Algorithm
    5. 10.4 Performance Evaluation
    6. 10.5 Discussion
  20. Chapter 11: Further readings
    1. Abstract
    2. 11.1 Estimation Theory
    3. 11.2 Data Quality and Trust Analysis
    4. 11.3 Outlier Analysis and Attack Detection
    5. 11.4 Recommender Systems
    6. 11.5 Surveys and Opinion Polling
  21. Chapter 12: Conclusions and future challenges
    1. Abstract
    2. 12.1 Summary and Conclusions
    3. 12.2 Remaining Challenges and Future Work
  22. References
  23. Index