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Truth discovery in crowdsourced detection of spatial events

Truth discovery in crowdsourced detection of spatial events
Truth discovery in crowdsourced detection of spatial events
The ubiquity of smartphones has led to the emergence of mobile crowdsourcing tasks such as the detection of spatial events when smartphone users move around in their daily lives. However, the credibility of those detected events can be negatively impacted by unreliable participants with low-quality data. Consequently, a major challenge in mobile crowdsourcing is truth discovery, i.e., to discover true events from diverse and noisy participants’ reports. This problem is uniquely distinct from its online counterpart in that it involves uncertainties in both participants’ mobility and reliability. Decoupling these two types of uncertainties through location tracking will raise severe privacy and energy issues, whereas simply ignoring missing reports or treating them as negative reports will significantly degrade the accuracy of truth discovery. In this paper, we propose two new unsupervised models, i.e., Truth finder for Spatial Events (TSE) and Personalized Truth finder for Spatial Events (PTSE), to tackle this problem. In TSE, we model location popularity, location visit indicators, truths of events, and three-way participant reliability in a unified framework. In PTSE, we further model personal location visit tendencies. These proposed models are capable of effectively handling various types of uncertainties and automatically discovering truths without any supervision or location tracking. Experimental results on both real-world and synthetic datasets demonstrate that our proposed models outperform existing state-of-the-art truth discovery approaches in the mobile crowdsourcing environment.
1041-4347
1047-1060
Ouyang, Robin Wentao
a0886331-0eed-46ee-9b72-843ef2bb192f
Srivastava, Mani
77c0ad90-6073-4dd8-908f-05087d856cd6
Toniolo, Alice
e54ad578-9232-471a-a5d7-cd3a7bc70872
Norman, Timothy
663e522f-807c-4569-9201-dc141c8eb50d
Ouyang, Robin Wentao
a0886331-0eed-46ee-9b72-843ef2bb192f
Srivastava, Mani
77c0ad90-6073-4dd8-908f-05087d856cd6
Toniolo, Alice
e54ad578-9232-471a-a5d7-cd3a7bc70872
Norman, Timothy
663e522f-807c-4569-9201-dc141c8eb50d

Ouyang, Robin Wentao, Srivastava, Mani, Toniolo, Alice and Norman, Timothy (2016) Truth discovery in crowdsourced detection of spatial events. IEEE Transactions on Knowledge and Data Engineering, 28 (4), 1047-1060. (doi:10.1109/TKDE.2015.2504928).

Record type: Article

Abstract

The ubiquity of smartphones has led to the emergence of mobile crowdsourcing tasks such as the detection of spatial events when smartphone users move around in their daily lives. However, the credibility of those detected events can be negatively impacted by unreliable participants with low-quality data. Consequently, a major challenge in mobile crowdsourcing is truth discovery, i.e., to discover true events from diverse and noisy participants’ reports. This problem is uniquely distinct from its online counterpart in that it involves uncertainties in both participants’ mobility and reliability. Decoupling these two types of uncertainties through location tracking will raise severe privacy and energy issues, whereas simply ignoring missing reports or treating them as negative reports will significantly degrade the accuracy of truth discovery. In this paper, we propose two new unsupervised models, i.e., Truth finder for Spatial Events (TSE) and Personalized Truth finder for Spatial Events (PTSE), to tackle this problem. In TSE, we model location popularity, location visit indicators, truths of events, and three-way participant reliability in a unified framework. In PTSE, we further model personal location visit tendencies. These proposed models are capable of effectively handling various types of uncertainties and automatically discovering truths without any supervision or location tracking. Experimental results on both real-world and synthetic datasets demonstrate that our proposed models outperform existing state-of-the-art truth discovery approaches in the mobile crowdsourcing environment.

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More information

Accepted/In Press date: 24 November 2015
e-pub ahead of print date: 3 December 2015
Published date: 1 April 2016
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 403233
URI: https://eprints.soton.ac.uk/id/eprint/403233
ISSN: 1041-4347
PURE UUID: 3bec8e2a-48d8-4964-82de-fd35a55de3d0
ORCID for Timothy Norman: ORCID iD orcid.org/0000-0002-6387-4034

Catalogue record

Date deposited: 28 Nov 2016 15:33
Last modified: 06 Jun 2018 12:19

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