Identifying spammers to boost crowdsourced classification

Panagiotis A. Traganitis; Georgios B. Giannakis

doi:10.1109/ICASSP39728.2021.9414242

Identifying spammers to boost crowdsourced classification

Panagiotis A. Traganitis, Georgios B. Giannakis

Electrical and Computer Engineering

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

Abstract

The present work addresses the problem of adversarial attacks in unsupervised ensemble or crowdsourcing classification tasks. Under certain conditions, it is shown, both analytically and through numerical tests, that spammers cause the most damage with respect to classification performance. To curb their effect, a novel spectral algorithm for spammer detection that utilizes second-order statistics of annotators, is developed and preliminary results on synthetic and real data showcase the potential of this approach.

Original language	English (US)
Pages (from-to)	2840-2844
Number of pages	5
Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2021-June
DOIs	https://doi.org/10.1109/ICASSP39728.2021.9414242
State	Published - 2021
Event	2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada Duration: Jun 6 2021 → Jun 11 2021

Bibliographical note

Funding Information:
Work in this paper was supported by NSF grant 1901134. Emails: traga003@umn.edu, georgios@umn.edu

Publisher Copyright:
©2021 IEEE

Keywords

Adversaries
Crowdsourcing
Ensemble learning
Spammers
Unsupervised

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AU - Giannakis, Georgios B.

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AB - The present work addresses the problem of adversarial attacks in unsupervised ensemble or crowdsourcing classification tasks. Under certain conditions, it is shown, both analytically and through numerical tests, that spammers cause the most damage with respect to classification performance. To curb their effect, a novel spectral algorithm for spammer detection that utilizes second-order statistics of annotators, is developed and preliminary results on synthetic and real data showcase the potential of this approach.

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KW - Unsupervised

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JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

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Y2 - 6 June 2021 through 11 June 2021

ER -

Identifying spammers to boost crowdsourced classification

Abstract

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Keywords

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