Identifying spammers to boost crowdsourced classification

Panagiotis A. Traganitis, Georgios B. Giannakis

Research output: Contribution to journalConference articlepeer-review

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 languageEnglish (US)
Pages (from-to)2840-2844
Number of pages5
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2021-June
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: Jun 6 2021Jun 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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