Sorry, Your HIT Is Overbooked - Investigating the Use of Crowdsourcing HIT Catchers

In microtask crowdsourcing, Human Intelligence Tasks (HITs) are commonly allocated on a first-come, first-served basis: they are published on the platform and the fastest workers select the most attractive ones first. This step has not received much attention from the scientific community yet, though it can become particularly taxing for workers when they compete to secure the most sought-after tasks. There are many strategies to ensure one's access to tasks and their effects on the labour process as a whole are not well understood. For instance, platforms with a sizeable task reservation queue allow workers to gain preferential access to a large number of tasks, which in turn may cause a shortage of work for the rest of the crowd. For the requesters, this means lower rates of completion and a lack of worker diversity. We explore workers' strategies for accessing and reserving tasks using monitoring techniques from both client and server sides. We investigate how these strategies affect task execution, in terms of availability, completion time, and answer quality, by deploying 1000 image annotation HITs in Amazon Mechanical Turk including objective and subjective tasks. We observe that workers who do not use automated catching techniques tend to have higher annotation quality, are more focused, spend more effort on text editing, and provide a higher diversity of output than workers using such tools. This study also reveals the tragedy of the commons effect among platform members due to the use of catching techniques: workers using automated catching techniques reserve and complete a substantially higher portion of the available tasks, but the over-reservation of HITs restricts all workers of reservation opportunities, and compromise their own future labour capacity as well. We observe a high inefficiency in job completions, as the majority of the times a task is being reserved by a worker, it will not get actually performed and will need to be republished for further allocation. Finally, we propose solutions to mitigate the negative effects of these phenomena on the labour process.

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Publication Details

Journal
Proceedings of the ACM on Human-Computer Interaction
Published
2025-10-16
DOI
https://doi.org/10.1145/3757426
Primary Topic
Mobile Crowdsensing and Crowdsourcing
Type
article
Field-Weighted Citation Impact
0.00
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article

Sorry, Your HIT Is Overbooked - Investigating the Use of Crowdsourcing HIT Catchers

Eddy Maddalena, Alessandro Checco, Elena Simperl, Efpraxia D. Zamani et al.
Proceedings of the ACM on Human-Computer Interaction
Mobile Crowdsensing and Crowdsourcing
article

Sorry, Your HIT Is Overbooked - Investigating the Use of Crowdsourcing HIT Catchers

Eddy Maddalena, Alessandro Checco, Elena Simperl, Efpraxia D. Zamani, Haoyu Xie
article en

Abstract

In microtask crowdsourcing, Human Intelligence Tasks (HITs) are commonly allocated on a first-come, first-served basis: they are published on the platform and the fastest workers select the most attractive ones first. This step has not received much attention from the scientific community yet, though it can become particularly taxing for workers when they compete to secure the most sought-after tasks. There are many strategies to ensure one's access to tasks and their effects on the labour process as a whole are not well understood. For instance, platforms with a sizeable task reservation queue allow workers to gain preferential access to a large number of tasks, which in turn may cause a shortage of work for the rest of the crowd. For the requesters, this means lower rates of completion and a lack of worker diversity. We explore workers' strategies for accessing and reserving tasks using monitoring techniques from both client and server sides. We investigate how these strategies affect task execution, in terms of availability, completion time, and answer quality, by deploying 1000 image annotation HITs in Amazon Mechanical Turk including objective and subjective tasks. We observe that workers who do not use automated catching techniques tend to have higher annotation quality, are more focused, spend more effort on text editing, and provide a higher diversity of output than workers using such tools. This study also reveals the tragedy of the commons effect among platform members due to the use of catching techniques: workers using automated catching techniques reserve and complete a substantially higher portion of the available tasks, but the over-reservation of HITs restricts all workers of reservation opportunities, and compromise their own future labour capacity as well. We observe a high inefficiency in job completions, as the majority of the times a task is being reserved by a worker, it will not get actually performed and will need to be republished for further allocation. Finally, we propose solutions to mitigate the negative effects of these phenomena on the labour process.

Proceedings of the ACM on Human-Computer InteractionVol. 9(7)
University of Udine (IT), King's College London (GB), Durham University (GB), Sapienza University of Rome (IT), University of Sheffield (GB)
Openalex Percentile: Top 67%
Mobile Crowdsensing and Crowdsourcing
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