Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media |
| Seiten | 115-122 |
| Seitenumfang | 8 |
| ISBN (elektronisch) | 9781450354271 |
| Publikationsstatus | Veröffentlicht - 3 Juli 2018 |
| Veranstaltung | 29th ACM International Conference on Hypertext and Social Media, HT 2018 - Baltimore, USA / Vereinigte Staaten Dauer: 9 Juli 2018 → 12 Juli 2018 |
Abstract
Workers in microtask crowdsourcing systems typically consume different types of tasks. Task consumption is driven by the selfselection of workers in the most popular platforms such as Amazon Mechanical Turk and CrowdFlower. Workers typically complete tasks one after another in a chain. Prior works have revealed the impact of ordering tasks while considering aspects such as task complexity. However, little is understood about the benefits of considering task similarity in microtask chains. In this paper, we investigate the role of task similarity in microtask crowdsourcing and how it affects market dynamics. We identified different dimensions that affect the perception of task similarity among workers, and propose a supervised machine learning model to predict the overall task similarity of a task pair. Leveraging task similarity, we studied the effects of similarity on worker retention, satisfaction, boredom and fatigue. We reveal the impact of chaining tasks according to their similarity on worker accuracy and their task completion time. Our findings enrich the current understanding of crowd work and bear important implications on structuring workflow.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Informatik (insg.)
- Artificial intelligence
- Informatik (insg.)
- Mensch-Maschine-Interaktion
- Informatik (insg.)
- Computergrafik und computergestütztes Design
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- BibTex
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HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media. 2018. S. 115-122.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
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TY - GEN
T1 - SimilarHITs
T2 - 29th ACM International Conference on Hypertext and Social Media, HT 2018
AU - Aipe, Alan
AU - Gadiraju, Ujwal
PY - 2018/7/3
Y1 - 2018/7/3
N2 - Workers in microtask crowdsourcing systems typically consume different types of tasks. Task consumption is driven by the selfselection of workers in the most popular platforms such as Amazon Mechanical Turk and CrowdFlower. Workers typically complete tasks one after another in a chain. Prior works have revealed the impact of ordering tasks while considering aspects such as task complexity. However, little is understood about the benefits of considering task similarity in microtask chains. In this paper, we investigate the role of task similarity in microtask crowdsourcing and how it affects market dynamics. We identified different dimensions that affect the perception of task similarity among workers, and propose a supervised machine learning model to predict the overall task similarity of a task pair. Leveraging task similarity, we studied the effects of similarity on worker retention, satisfaction, boredom and fatigue. We reveal the impact of chaining tasks according to their similarity on worker accuracy and their task completion time. Our findings enrich the current understanding of crowd work and bear important implications on structuring workflow.
AB - Workers in microtask crowdsourcing systems typically consume different types of tasks. Task consumption is driven by the selfselection of workers in the most popular platforms such as Amazon Mechanical Turk and CrowdFlower. Workers typically complete tasks one after another in a chain. Prior works have revealed the impact of ordering tasks while considering aspects such as task complexity. However, little is understood about the benefits of considering task similarity in microtask chains. In this paper, we investigate the role of task similarity in microtask crowdsourcing and how it affects market dynamics. We identified different dimensions that affect the perception of task similarity among workers, and propose a supervised machine learning model to predict the overall task similarity of a task pair. Leveraging task similarity, we studied the effects of similarity on worker retention, satisfaction, boredom and fatigue. We reveal the impact of chaining tasks according to their similarity on worker accuracy and their task completion time. Our findings enrich the current understanding of crowd work and bear important implications on structuring workflow.
KW - Crowdsourcing
KW - Microtasks
KW - Performance
KW - Task similarity
KW - Workers
UR - http://www.scopus.com/inward/record.url?scp=85051497034&partnerID=8YFLogxK
U2 - 10.1145/3209542.3209558
DO - 10.1145/3209542.3209558
M3 - Conference contribution
AN - SCOPUS:85051497034
SP - 115
EP - 122
BT - HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media
Y2 - 9 July 2018 through 12 July 2018
ER -