The Success and Failure of Quality Improvement Projects in Peer Production Communities

Warncke-Wang, Morten; Ayukaev, Vladislav R.; Hecht, Brent; Terveen, Loren

doi:10.1145/2675133.2675241

Cited by 47 publications

(76 citation statements)

References 44 publications

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“…First, our study reiterated recent findings (Warncke-Wang, Ayukaev, Hecht, & Terveen 2015) that demonstrate the limits of notification as a method for filling gaps in peer production projects but produced new insight on why this may be the case. The majority of WikiProjects no longer fulfil the goal of coordinating editing tasks and so notification cannot catalyse the filling of prioritised tasks on Wikipedia.…”

Section: Resultssupporting

confidence: 76%

Beyond notification: Filling gaps in peer production projects

Ford

Pensa²,

Devouard³

et al. 2018

New Media & Society

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In order to counter systemic bias in peer production projects like Wikipedia, a variety of strategies have been used to fill gaps and improve the completeness of the archive. We test a number of these strategies in a project aimed at improving articles relating to South Africa’s primary school curriculum and find that many of the predominant strategies are insufficient for filling Wikipedia’s gaps. Notifications that alert users to the existence of gaps including incomplete or missing articles, in particular, are found to be ineffective at improving articles. Only through the process of trust-building and the development of negotiated boundary objects, potential allies (institutional academics in this case) can be enrolled in the task of editing the encyclopaedia. Rather than a simple process of enrolment via notification, this project demonstrated the principles of negotiation required for engaging with new editor groups in the long-term project of filling Wikipedia’s gaps

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Section: Resultssupporting

confidence: 76%

Beyond notification: Filling gaps in peer production projects

Ford

Pensa²,

Devouard³

et al. 2018

New Media & Society

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show abstract

“…For instance, the information presented on Wikipedia is not accepted as a reliable source for research by many professors and researchers [5]. The main problem is that, while many articles are of high quality, many others did not receive the desired attention from authors to improve their quality [6], [7].…”

Section: Introductionmentioning

confidence: 99%

“…The success and failure of these projects are 1 https://reportcard.wmflabs.org/ discussed in [7]. Wikipedia development team also implemented different kinds of bots 2 to execute several automatic tasks, such as checking if a submitted revision damages a particular Wikipedia page or not [8].…”

Section: Introductionmentioning

confidence: 99%

Measuring Quality of Collaboratively Edited Documents: The Case of Wikipedia

Dang

Ignat

2016

2016 IEEE 2nd International Conference on Collaboration and Internet Computing (CIC)

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Abstract-Wikipedia is a great example of large scale collaboration, where people from all over the world together build the largest and maybe the most important human knowledge repository in the history. However, a number of studies showed that the quality of Wikipedia articles is not equally distributed. While many articles are of good quality, many others need to be improved. Assessing the quality of Wikipedia articles is very important for guiding readers towards articles of high quality and suggesting authors and reviewers which articles need to be improved. Due to the huge size of Wikipedia, an effective automatic assessment method to measure Wikipedia articles quality is needed.In this paper, we present an automatic assessment method of Wikipedia articles quality by analyzing their content in terms of their format features and readability scores. Our results show improvements both in terms of accuracy and information gain compared with other existing approaches.

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“…This is notwithstanding findings that even when trust is important, experts also make mistakes [20]. In citizen science projects, peer participants and trusted participants often help in identifying and validating the observations provided by new volunteers [31,32]. This peer verification enables some control and constitutes a form of QA by crowdsourcing [8] the data and indirectly the volunteer who captured the data.…”

Section: Trustworthiness and Data Qualitymentioning

confidence: 99%

“…Therefore, a proper initial QA can be paramount to whatever single DCDF is to be performed or whatever iterative DCP process takes place (see Section 4). Figure 1 highlights the output variations obtained for each combination of uncertainty levels set up for the multiway sensitivity analysis [23] of the Flickr inundation extent workflow DCDF [31]. A Monte Carlo uncertainty was run for each combination of input uncertainty.…”

Section: Storage Usage Re-usagementioning

confidence: 99%

On Data Quality Assurance and Conflation Entanglement in Crowdsourcing for Environmental Studies

Leibovici

Rosser

Hodges

et al. 2017

IJGI

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Volunteer geographical information (VGI), either in the context of citizen science or the mining of social media, has proven to be useful in various domains including natural hazards, health status, disease epidemics, and biological monitoring. Nonetheless, the variable or unknown data quality due to crowdsourcing settings are still an obstacle for fully integrating these data sources in environmental studies and potentially in policy making. The data curation process, in which a quality assurance (QA) is needed, is often driven by the direct usability of the data collected within a data conflation process or data fusion (DCDF), combining the crowdsourced data into one view, using potentially other data sources as well. Looking at current practices in VGI data quality and using two examples, namely land cover validation and inundation extent estimation, this paper discusses the close links between QA and DCDF. It aims to help in deciding whether a disentanglement can be possible, whether beneficial or not, in understanding the data curation process with respect to its methodology for future usage of crowdsourced data. Analysing situations throughout the data curation process where and when entanglement between QA and DCDF occur, the paper explores the various facets of VGI data capture, as well as data quality assessment and purposes. Far from rejecting the usability ISO quality criterion, the paper advocates for a decoupling of the QA process and the DCDF step as much as possible while still integrating them within an approach analogous to a Bayesian paradigm.

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The Success and Failure of Quality Improvement Projects in Peer Production Communities

Cited by 47 publications

References 44 publications

Beyond notification: Filling gaps in peer production projects

Beyond notification: Filling gaps in peer production projects

Measuring Quality of Collaboratively Edited Documents: The Case of Wikipedia

On Data Quality Assurance and Conflation Entanglement in Crowdsourcing for Environmental Studies

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