Social Sensing: Building Reliable Systems on Unreliable Data

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 reliabilityPresents novel theoretical foundations for assured social sensing and modeling humans as sensorsIncludes case studies and application examples based on real data setsSupplemental material includes sample datasets and fact-finding software that implements the main algorithms described in the book

1. Introduction
2. Social Sensing Trends and Applications
3. Mathematical Foundations
4. Basic Fact-Finding
5. Maximum Likelihood Estimation
6. Confidence Bounds
7. Conflicting Observations and Non-Binary Claims
8. Understanding the Social Network
9. Understanding Physical Dependencies
10. Recursive Fact-finding
11. Privacy
12. Further Readings
13. Conclusions and Remaining Challenges