NDLI: On truth discovery in social sensing: a maximum likelihood estimation approach

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On truth discovery in social sensing: a maximum likelihood estimation approach

Content Provider	ACM Digital Library
Author	Wang, Dong Abdelzaher, Tarek Kaplan, Lance Le, Hieu
Abstract	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.
Starting Page	233
Ending Page	244
Page Count	12
File Format	PDF
ISBN	9781450312271
DOI	10.1145/2185677.2185737
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-04-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Maximum likelihood estimation Social sensing Expectation maximization Truth discovery
Content Type	Text
Resource Type	Article

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