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Cited by 28Mobile Crowdsensing and CrowdsourcingMachine Learning and Data ClassificationExpert finding and Q&A systems

A technical survey on statistical modelling and design methods for crowdsourcing quality control

Open access
Jin, Yuan, Carman, Mark, Zhu, Ye, Xiang, Yong·2020·Artificial Intelligence
DOI 10.1016/j.artint.2020.103351
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Crossref · 10.1016/j.artint.2020.103351

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  • Most existing aggregation methods focus on weighting worker contributions without accounting for contextual information, though other methods introduce context-aware mechanisms.

    “Most existing aggregation methods focus on weighting worker contributions without accounting for contextual information (Zhang et al., 2016;Zheng et al., 2017;Zhang, 2022)), though other methods introduce context-aware mechanisms (Jin et al., 2020;Zhang, 2022).”

    From SkillAggregation: Reference-free LLM-Dependent Aggregation (2025) · cites this paper as Jin et al., 2020

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Authors
Jin, Y., Carman, M., Zhu, Y., & Xiang, Y.
Affiliations
  • Monash University (AU)
  • Politecnico di Milano (IT)
  • Deakin University (AU)
Corresponding author
Jin, Yuan
ORCID
Venue
Artificial Intelligence
Publisher
Elsevier BV
Published
2020-10
Volume
287
Pages
103351
ISSN
0004-3702 (ISSN-L)
DOI
10.1016/j.artint.2020.103351
OpenAlex
W3038629767
Cited by
28
References
6
Citations by year
2026: 1 · 2025: 5 · 2024: 3 · 2023: 7 · 2022: 4 · 2021: 5 · 2020: 1 · 2019: 2
FWCI
3.73
Type
journal article
Language
en
Open access
Licence
Locations
  • Artificial Intelligence (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, other-oa)
  • Figshare (submittedVersion, cc-by)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
  • Physical Sciences › Computer Science › Information Systems › Expert finding and Q&A systems
Keywords
response quality, response aggregation, employee behavior, gamification, payment mechanisms, performance guarantees

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