Understanding Workers, Developing Effective Tasks, and Enhancing Marketplace Dynamics: A Study of a Large Crowdsourcing Marketplace
Summary: Empirical analysis of 27M microtasks by 70K+ workers over four years in a major crowdsourcing marketplace. Reveals how task design shapes worker behavior, marketplace load, attention spans, and worker lifetimes, informing ecosystem design. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ayush Jain (University of Illinois Urbana-Champaign)
- 2. Akash Das Sarma (Stanford University)
- 3. Aditya Parameswaran (University of Illinois Urbana-Champaign)
- 4. Jennifer Widom (Stanford University)
BibTeX Citation
@article{jain_vldb17,
title = {{Understanding Workers, Developing Effective Tasks, and Enhancing Marketplace Dynamics: A Study of a Large Crowdsourcing Marketplace}},
author = {Jain, Ayush and Sarma, Akash Das and Parameswaran, Aditya and Widom, Jennifer},
journal = {PVLDB},
series = {{VLDB} '17},
volume = {10},
number = {7},
pages = {829--842},
doi = {10.14778/3067421.3067431},
url = {https://doi.org/10.14778/3067421.3067431},
year = {2017}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,019 | RPT: Relational Pre-trained Transformer Is Almost All You Need towards Democratizing Data Preparation | 2021 | VLDB | 9.2983994e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 90 | CrowdDB: Answering Queries with Crowdsourcing | 2011 | SIGMOD | 0.00034951786 |
| 251 | Crowdsourced Databases: Query Processing with People | 2011 | CIDR | 0.00023261113 |
| 743 | So Who Won? Dynamic Max Discovery with the Crowd | 2012 | SIGMOD | 0.00014421358 |
| 1,270 | CDAS: A Crowdsourcing Data Analytics System | 2012 | VLDB | 0.00011388453 |
| 2,789 | Crowd Mining | 2013 | SIGMOD | 8.1203892e-05 |
| 3,914 | Optimal Crowd-Powered Rating and Filtering Algorithms | 2014 | VLDB | 7.0204052e-05 |
| 7,665 | Towards Globally Optimal Crowdsourcing Quality Management: The Uniform Worker Setting | 2016 | SIGMOD | 5.5718685e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 5,143 | An Online Cost Sensitive Decision-Making Method in Crowdsourcing Systems | 2013 | SIGMOD |
| 2 | 12,068 | Collaborative Crowdsourcing with Crowd4U | 2016 | VLDB |
| 3 | 7,417 | An Experimental Evaluation of Task Assignment in Spatial Crowdsourcing | 2018 | VLDB |
| 4 | 3,262 | iCrowd: An Adaptive Crowdsourcing Framework | 2015 | SIGMOD |
| 5 | 12,101 | The Importance of Being Expert: Efficient Max-Finding in Crowdsourcing | 2015 | SIGMOD |
| 6 | 11,788 | Recommending Deployment Strategies for Collaborative Tasks | 2020 | SIGMOD |
| 7 | 11,847 | Recommending Deployment Strategies in Crowdsourcing Platforms | 2019 | SIGMOD |
| 8 | 11,925 | Crowdsourcing Analytics with CrowdCur | 2018 | SIGMOD |
| 9 | 7,222 | Hear the Whole Story: Towards the Diversity of Opinion in Crowdsourcing Markets | 2015 | VLDB |
| 10 | 10,029 | Human Factors in Crowdsourcing | 2016 | VLDB |