The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
IEEE Transactions on Knowledge and Data Engineering
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
CenWits: a sensor-based loosely coupled search and rescue system using witnesses
Proceedings of the 3rd international conference on Embedded networked sensor systems
CarTel: a distributed mobile sensor computing system
Proceedings of the 4th international conference on Embedded networked sensor systems
Diffusion dynamics in small-world networks with heterogeneous consumers
Computational & Mathematical Organization Theory
IEEE Pervasive Computing
The BikeNet mobile sensing system for cyclist experience mapping
Proceedings of the 5th international conference on Embedded networked sensor systems
Truth Discovery with Multiple Conflicting Information Providers on the Web
IEEE Transactions on Knowledge and Data Engineering
Get another label? improving data quality and data mining using multiple, noisy labelers
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 7th international conference on Mobile systems, applications, and services
Ranking-based clustering of heterogeneous information networks with star network schema
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Context Dependent Movie Recommendations Using a Hierarchical Bayesian Model
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Truth discovery and copying detection in a dynamic world
Proceedings of the VLDB Endowment
Corroborating information from disagreeing views
Proceedings of the third ACM international conference on Web search and data mining
Biketastic: sensing and mapping for better biking
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Privacy-aware regression modeling of participatory sensing data
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Knowing what to believe (when you already know something)
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Global detection of complex copying relationships between sources
Proceedings of the VLDB Endowment
Semi-supervised truth discovery
Proceedings of the 20th international conference on World wide web
SourceRank: relevance and trust assessment for deep web sources based on inter-source agreement
Proceedings of the 20th international conference on World wide web
The Sparse Regression Cube: A Reliable Modeling Technique for Open Cyber-Physical Systems
ICCPS '11 Proceedings of the 2011 IEEE/ACM Second International Conference on Cyber-Physical Systems
Recruitment framework for participatory sensing data collections
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
Simulating the Diffusion of Information: An Agent-Based Modeling Approach
International Journal of Agent Technologies and Systems
Free-form text summarization in social sensing
Proceedings of the 11th international conference on Information Processing in Sensor Networks
Detection, classification and visualization of place-triggered geotagged tweets
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
On assessing the accuracy of positioning systems in indoor environments
EWSN'13 Proceedings of the 10th European conference on Wireless Sensor Networks
Maximum likelihood analysis of conflicting observations in social sensing
ACM Transactions on Sensor Networks (TOSN)
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This paper addresses the challenge of truth discovery from noisy social sensing data. The work is motivated by the emergence of social sensing as a data collection paradigm of growing interest, where humans perform sensory data collection tasks. A challenge in social sensing applications lies in the noisy nature of data. Unlike the case with well-calibrated and well-tested infrastructure sensors, humans are less reliable, and the likelihood that participants' measurements are correct is often unknown a priori. Given a set of human participants of unknown reliability together with their sensory measurements, this paper poses the question of whether one can use this information alone to determine, in an analytically founded manner, the probability that a given measurement is true. The paper focuses on binary measurements. While some previous work approached the answer in a heuristic manner, we offer the first optimal solution to the above truth discovery problem. Optimality, in the sense of maximum likelihood estimation, is attained by solving an expectation maximization problem that returns the best guess regarding the correctness of each measurement. The approach is shown to outperform the state of the art fact-finding heuristics, as well as simple baselines such as majority voting.