2020
DOI: 10.47756/aihc.y5i1.65
|View full text |Cite
|
Sign up to set email alerts
|

Understanding the complementary nature of paid and volunteer crowds for content creation

Abstract: Crowdsourced content creation like articles or slogans can be powered by crowds of volunteers or workers from paid task markets. Volunteers often have expertise and are intrinsically motivated, but are a limited resource, and are not always reliably available. On the other hand, paid crowd workers are reliably available, can be guided to produce high-quality content, but cost money. How can these different populations of crowd workers be leveraged together to power cost-effective yet high-quality crowd-powered… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

1
3
0

Year Published

2022
2022
2023
2023

Publication Types

Select...
2
1

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(4 citation statements)
references
References 11 publications
1
3
0
Order By: Relevance
“…Crowd workers can demonstrate high quality on relatively simple data curation tasks, such as correcting intents. This result aligns with previous studies that showed how crowd workers can follow instructions but produce less original content (Flores-Saviaga et al 2020). Correspondingly, experts authored better dialogue with higher creativity and more emotion.…”
Section: Discussionsupporting
confidence: 91%
See 2 more Smart Citations
“…Crowd workers can demonstrate high quality on relatively simple data curation tasks, such as correcting intents. This result aligns with previous studies that showed how crowd workers can follow instructions but produce less original content (Flores-Saviaga et al 2020). Correspondingly, experts authored better dialogue with higher creativity and more emotion.…”
Section: Discussionsupporting
confidence: 91%
“…Some prior work has started to unpack the tradeoffs of using novice crowds vs. experts for data work. For example, Flores et al investigated the performance of paid crowd workers and volunteers on a content curation task (Flores-Saviaga et al 2020). Results indicate that volunteer collaborators are more effective at open-ended tasks, while paid crowd workers are more effective at decomposed tasks.…”
Section: Novices Versus Experts For Ai Data Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Additionally, exploring the impact of various worker backgrounds, such as age [56], gender [57], personality [55] and expertise [29,32,33], on the effectiveness of facial verification and other types of tasks would provide valuable insights. Additionally, examining the role of monetary incentives on crowdworkers' performance in facial verification tasks could also be beneficial, as it has been shown to affect task quality in other contexts [30,77]. A comprehensive understanding of how these factors interact with own-race bias can facilitate the development of more equitable and accurate facial verification systems.…”
Section: Utilizingmentioning
confidence: 99%