Results 1 - 10
of
10
Crowds, not Drones: Modeling Human Factors in Interactive Crowdsourcing
"... In this vision paper, we propose SmartCrowd, an intelligent and adaptive crowdsourcing framework. Contrary to exist-ing crowdsourcing systems, where the process of hiring work-ers (crowd), learning their skills, and evaluating the accu-racy of tasks they perform are fragmented, siloed, and often ad- ..."
Abstract
-
Cited by 3 (3 self)
- Add to MetaCart
(Show Context)
In this vision paper, we propose SmartCrowd, an intelligent and adaptive crowdsourcing framework. Contrary to exist-ing crowdsourcing systems, where the process of hiring work-ers (crowd), learning their skills, and evaluating the accu-racy of tasks they perform are fragmented, siloed, and often ad-hoc, SmartCrowd foresees a paradigm shift in that pro-cess, considering unpredictability of human nature, namely human factors. SmartCrowd offers opportunities in making crowdsourcing intelligent through iterative interaction with the workers, and adaptively learning and improving the un-derlying processes. Both existing (majority of which do not require longer engagement from volatile and mostly non-recurrent workers) and next generation crowdsourcing appli-cations (which require longer engagement from the crowd) stand to benefit from SmartCrowd. We outline the opportu-nities in SmartCrowd, and discuss the challenges and direc-tions, that can potentially revolutionize the existing crowd-sourcing landscape. 1.
What Will Others Choose? How a Majority Vote Reward Scheme Can Improve Human Computation in a Spatial Location Identification Task
"... We created a spatial location identification task (SpLIT) in which workers recruited from Amazon Mechanical Turk were presented with a camera view of a location, and were asked to identify the location on a two-dimensional map. In cases where these cues were ambiguous or did not provide enough infor ..."
Abstract
-
Cited by 2 (1 self)
- Add to MetaCart
We created a spatial location identification task (SpLIT) in which workers recruited from Amazon Mechanical Turk were presented with a camera view of a location, and were asked to identify the location on a two-dimensional map. In cases where these cues were ambiguous or did not provide enough information to pinpoint the exact location, workers had to make a best guess. We tested the effects of two reward schemes. In the “ground truth ” scheme, workers were rewarded if their answers were close enough to the correct locations. In the “majority vote ” scheme, workers were told that they would be rewarded if their answers were similar to the majority of other workers. Results showed that the majority vote reward scheme led to consistently more accurate answers. Cluster analysis further showed that the majority vote reward scheme led to answers with higher reliability (a higher percentage of answers in the correct clusters) and precision (a smaller average distance to the cluster centers). Possible reasons for why the majority voting reward scheme was better were discussed.
A Comparison of Social, Learning, and Financial Strategies on Crowd Engagement and Output Quality
"... A significant challenge for crowdsourcing has been increas-ing worker engagement and output quality. We explore the effects of social, learning, and financial strategies, and their combinations, on increasing worker retention across tasks and change in the quality of worker output. Through three exp ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
(Show Context)
A significant challenge for crowdsourcing has been increas-ing worker engagement and output quality. We explore the effects of social, learning, and financial strategies, and their combinations, on increasing worker retention across tasks and change in the quality of worker output. Through three experiments, we show that 1) using these strategies together increased workers ’ engagement and the quality of their work; 2) a social strategy was most effective for increasing engagement; 3) a learning strategy was most effective in improving quality. The findings of this paper provide strate-gies for harnessing the crowd to perform complex tasks, as well as insight into crowd workers ’ motivation. Author Keywords Crowds; incentives; strategies; motivations
unknown title
"... Abstract-Most current microtask crowdsourcing platforms do not exploit the individual expertise of workers, which becomes extremely relevant for knowledge-intensive microtasks in human computation scenarios. In this paper, we discuss work in progress on worker profiling within microtask platforms t ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract-Most current microtask crowdsourcing platforms do not exploit the individual expertise of workers, which becomes extremely relevant for knowledge-intensive microtasks in human computation scenarios. In this paper, we discuss work in progress on worker profiling within microtask platforms to increase both the quality of the work and the satisfaction of the users. We analyse the issue of profiling workers and propose the introduction of a crowd worker CV as a comprehensive means to describe a worker's expertise and interests. We discuss several important dimensions that should be included in such a CV and analyse their benefits.
Recover Faster from Disaster: Success Factors for a Crowdsourcing Platform Martijn Neef
"... ABSTRACT In this paper, we present a model that identifies seven success factors for the development of crowdsourcing platforms for disaster recovery. This model integrates two existing theories. The first theory focuses on success factors of crowdsourcing initiatives in general. The second theory ..."
Abstract
- Add to MetaCart
ABSTRACT In this paper, we present a model that identifies seven success factors for the development of crowdsourcing platforms for disaster recovery. This model integrates two existing theories. The first theory focuses on success factors of crowdsourcing initiatives in general. The second theory states how disaster relief operations can improve when they take the psychological components of resilience into account. By merging the core principles of these two theories and adding additional knowledge gained from literature study, we constructed an integrated success factor model for use in the development of crowdsourcing applications for disaster recovery. An initial validation of the success factor model was conducted within a case study on a crowdsourcing platform for disaster recovery which is currently being developed. Conclusions are drawn with regards to the applicability of the model to guide development of crowdsourcing platforms for disaster recovery.
UW Tacoma,
"... In this vision paper, we propose SmartCrowd, an intelligent and adaptive crowdsourcing framework. Contrary to existing crowdsourcing systems, where the process of hiring workers (crowd), learning their skills, and evaluating the accuracy of tasks they perform are fragmented, siloed, and often ad-hoc ..."
Abstract
- Add to MetaCart
(Show Context)
In this vision paper, we propose SmartCrowd, an intelligent and adaptive crowdsourcing framework. Contrary to existing crowdsourcing systems, where the process of hiring workers (crowd), learning their skills, and evaluating the accuracy of tasks they perform are fragmented, siloed, and often ad-hoc, SmartCrowd foresees a paradigm shift in that process, considering unpredictability of human nature, namely human factors. SmartCrowd offers opportunities in making crowdsourcing intelligent through iterative interaction with the workers, and adaptively learning and improving the underlying processes. Both existing (majority of which do not require longer engagement from volatile and mostly nonrecurrent workers) and next generation crowdsourcing applications (which require longer engagement from the crowd) stand to benefit from SmartCrowd. We outline the opportunities in SmartCrowd, and discuss the challenges and directions, that can potentially revolutionize the existing crowdsourcing landscape. 1.
Contents lists available at ScienceDirect
"... Computer N journal homepage: www.else PRINGL – A domain-specific language fo management in crowdsourcing✩ ..."
Abstract
- Add to MetaCart
(Show Context)
Computer N journal homepage: www.else PRINGL – A domain-specific language fo management in crowdsourcing✩
1Reliable Crowdsourcing for Multi-Class Labeling using Coding Theory
"... Crowdsourcing systems often have crowd workers that perform unreliable work on the task they are assigned. In this paper, we propose the use of error-control codes and decoding algorithms to design crowdsourcing systems for reliable classification despite unreliable crowd workers. Coding-theory base ..."
Abstract
- Add to MetaCart
(Show Context)
Crowdsourcing systems often have crowd workers that perform unreliable work on the task they are assigned. In this paper, we propose the use of error-control codes and decoding algorithms to design crowdsourcing systems for reliable classification despite unreliable crowd workers. Coding-theory based techniques also allow us to pose easy-to-answer binary questions to the crowd workers. We consider three different crowdsourcing models: systems with independent crowd workers, systems with peer-dependent reward schemes, and systems where workers have common sources of information. For each of these models, we analyze classification performance with the proposed coding-based scheme. We develop an ordering principle for the quality of crowds and describe how system performance changes with the quality of the crowd. We also show that pairing among workers and diversification of the questions help in improving system performance. We demonstrate the effectiveness of the proposed coding-based scheme using both simulated data and real datasets from Amazon Mechanical Turk, a crowdsourcing microtask platform. Results suggest that use of good codes may improve the performance of the crowdsourcing task over typical majority-voting approaches. Index Terms crowdsourcing, error-control codes, multi-class labeling, quality assurance I.
Recommended Citation
, 2014
"... This dissertation is an in-depth case study of NATO advisors and their perceived influence in Afghanistan (2009-2012). It explores the two-part question, how do foreign security actors (ministerial advisors and security force trainers, advisors, and commanders) attempt to influence their host-nation ..."
Abstract
- Add to MetaCart
(Show Context)
This dissertation is an in-depth case study of NATO advisors and their perceived influence in Afghanistan (2009-2012). It explores the two-part question, how do foreign security actors (ministerial advisors and security force trainers, advisors, and commanders) attempt to influence their host-nation partners and what are their perceptions of these approaches on changes in local capacity, values, and security governance norms? I argue that security sector reform (SSR) programs in fragile states lack an explicit theory of change that specifies how reform occurs. From this view, I theorize internationally led SSR as “guided institutional transfer, ” grounded in rationalist and social constructivist explanations of convergence, diffusion, and socialization processes. Responding to calls for greater depth and emphasis on interactions and institutional change in SSR research, I examine NATO’s efforts in Afghanistan as an extreme case of SSR in which external-internal interactions were the highest. A stratified, purposive sample of 68 military and civilian elites (24 ministerial advisors, 27 embedded field advisors and commanders, and 17 experts and external observers) participated in a confidential, semi-structured interview.
What Will Others Choose? How a Majority Vote Reward Scheme Can Improve Human Computation in a Spatial Location Identification Task
"... We created a spatial location identification task (SpLIT) in which workers recruited from Amazon Mechanical Turk were presented with a camera view of a location, and were asked to identify the location on a two-dimensional map. In cases where these cues were ambiguous or did not provide enough infor ..."
Abstract
- Add to MetaCart
We created a spatial location identification task (SpLIT) in which workers recruited from Amazon Mechanical Turk were presented with a camera view of a location, and were asked to identify the location on a two-dimensional map. In cases where these cues were ambiguous or did not provide enough information to pinpoint the exact location, workers had to make a best guess. We tested the effects of two reward schemes. In the “ground truth ” scheme, workers were re-warded if their answers were close enough to the correct lo-cations. In the “majority vote ” scheme, workers were told that they would be rewarded if their answers were similar to the majority of other workers. Results showed that the major-ity vote reward scheme led to consistently more accurate an-swers. Cluster analysis further showed that the majority vote reward scheme led to answers with higher reliability (a higher percentage of answers in the correct clusters) and precision (a smaller average distance to the cluster centers). Possible rea-sons for why the majority voting reward scheme was better were discussed.