Truth Discovery in Crowdsourced Detection of Spatial Events

Robin Wentao Ouyang, Mani Srivastava, Alice Toniolo, Timothy J Norman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)
51 Downloads (Pure)

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 quality control is to discover true events from diverse and noisy participants’ reports. This truth discovery 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 the discovered truth. In this paper, we propose a new method to tackle this truth discovery problem through principled probabilistic modeling. In particular, we integrate the modeling of location popularity, location visit indicators, truth of events and three-way participant reliability in a unified framework. The proposed model is thus capable of efficiently handling various types of uncertainties and automatically discovering truth without any supervision or the need of location tracking. Experimental results demonstrate that our proposed method out-performs existing state-of-the-art truth discovery approaches in the mobile crowdsourcing environment.
Original languageEnglish
Title of host publicationProceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management
Place of PublicationNew York, NY, USA
PublisherACM
Pages461-470
Number of pages10
ISBN (Print)978-1-4503-2598-1
DOIs
Publication statusPublished - 2014

Fingerprint

Smartphones
Quality control
Uncertainty

Keywords

  • Mobile crowdsourcing
  • quality control
  • graphical models
  • data mining

ASJC Scopus subject areas

  • Information Systems

Cite this

Ouyang, R. W., Srivastava, M., Toniolo, A., & Norman, T. J. (2014). Truth Discovery in Crowdsourced Detection of Spatial Events. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (pp. 461-470). New York, NY, USA: ACM. https://doi.org/10.1145/2661829.2662003

Truth Discovery in Crowdsourced Detection of Spatial Events. / Ouyang, Robin Wentao; Srivastava, Mani; Toniolo, Alice; Norman, Timothy J.

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. New York, NY, USA : ACM, 2014. p. 461-470.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ouyang, RW, Srivastava, M, Toniolo, A & Norman, TJ 2014, Truth Discovery in Crowdsourced Detection of Spatial Events. in Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. ACM, New York, NY, USA, pp. 461-470. https://doi.org/10.1145/2661829.2662003
Ouyang RW, Srivastava M, Toniolo A, Norman TJ. Truth Discovery in Crowdsourced Detection of Spatial Events. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. New York, NY, USA: ACM. 2014. p. 461-470 https://doi.org/10.1145/2661829.2662003
Ouyang, Robin Wentao ; Srivastava, Mani ; Toniolo, Alice ; Norman, Timothy J. / Truth Discovery in Crowdsourced Detection of Spatial Events. Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. New York, NY, USA : ACM, 2014. pp. 461-470
@inproceedings{7171d0d23433491d85fd4902043989e0,
title = "Truth Discovery in Crowdsourced Detection of Spatial Events",
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 quality control is to discover true events from diverse and noisy participants’ reports. This truth discovery 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 the discovered truth. In this paper, we propose a new method to tackle this truth discovery problem through principled probabilistic modeling. In particular, we integrate the modeling of location popularity, location visit indicators, truth of events and three-way participant reliability in a unified framework. The proposed model is thus capable of efficiently handling various types of uncertainties and automatically discovering truth without any supervision or the need of location tracking. Experimental results demonstrate that our proposed method out-performs existing state-of-the-art truth discovery approaches in the mobile crowdsourcing environment.",
keywords = "Mobile crowdsourcing, quality control, graphical models, data mining",
author = "Ouyang, {Robin Wentao} and Mani Srivastava and Alice Toniolo and Norman, {Timothy J}",
year = "2014",
doi = "10.1145/2661829.2662003",
language = "English",
isbn = "978-1-4503-2598-1",
pages = "461--470",
booktitle = "Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management",
publisher = "ACM",

}

TY - GEN

T1 - Truth Discovery in Crowdsourced Detection of Spatial Events

AU - Ouyang, Robin Wentao

AU - Srivastava, Mani

AU - Toniolo, Alice

AU - Norman, Timothy J

PY - 2014

Y1 - 2014

N2 - 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 quality control is to discover true events from diverse and noisy participants’ reports. This truth discovery 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 the discovered truth. In this paper, we propose a new method to tackle this truth discovery problem through principled probabilistic modeling. In particular, we integrate the modeling of location popularity, location visit indicators, truth of events and three-way participant reliability in a unified framework. The proposed model is thus capable of efficiently handling various types of uncertainties and automatically discovering truth without any supervision or the need of location tracking. Experimental results demonstrate that our proposed method out-performs existing state-of-the-art truth discovery approaches in the mobile crowdsourcing environment.

AB - 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 quality control is to discover true events from diverse and noisy participants’ reports. This truth discovery 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 the discovered truth. In this paper, we propose a new method to tackle this truth discovery problem through principled probabilistic modeling. In particular, we integrate the modeling of location popularity, location visit indicators, truth of events and three-way participant reliability in a unified framework. The proposed model is thus capable of efficiently handling various types of uncertainties and automatically discovering truth without any supervision or the need of location tracking. Experimental results demonstrate that our proposed method out-performs existing state-of-the-art truth discovery approaches in the mobile crowdsourcing environment.

KW - Mobile crowdsourcing

KW - quality control

KW - graphical models

KW - data mining

U2 - 10.1145/2661829.2662003

DO - 10.1145/2661829.2662003

M3 - Conference contribution

SN - 978-1-4503-2598-1

SP - 461

EP - 470

BT - Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

PB - ACM

CY - New York, NY, USA

ER -