Difference between revisions of "Automated Decision Support for Human Tasks in a Collaborative System: the Case of Deletion in Wikipedia"
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| + | | title = Automated Decision Support for Human Tasks in a Collaborative System: the Case of Deletion in Wikipedia | ||
| + | | date = 2013 | ||
| + | | authors = [[Bluma Gelley]]<br />[[Torsten Suel]] | ||
| + | | doi = 10.1145/2491055.2491084 | ||
| + | | link = http://dl.acm.org/citation.cfm?doid=2491055.2491084 | ||
| + | }} | ||
'''Automated Decision Support for Human Tasks in a Collaborative System: the Case of Deletion in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Bluma Gelley]] and [[Torsten Suel]]. | '''Automated Decision Support for Human Tasks in a Collaborative System: the Case of Deletion in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Bluma Gelley]] and [[Torsten Suel]]. | ||
== Overview == | == Overview == | ||
Wikipedia's low barriers to participation have the unintended effect of attracting a large number of articles whose topics do not meet [[Wikipedia]]'s inclusion standards. Many are quickly deleted, often causing their creators to stop contributing to the site. Authors collect and make available several datasets of deleted articles, heretofore inaccessible, and use them to create a model that can predict with high precision whether or not an article will be deleted. Authors report precision of 98.6% and recall of 97.5% in the best case and high precision with lower, but still useful, recall, in the most difficult case. Authors propose to deploy a system utilizing this model on Wikipedia as a set of decision-support tools to help article creators evaluate and improve their articles before posting, and new article patrollers make more informed decisions about which articles to delete and which to improve. | Wikipedia's low barriers to participation have the unintended effect of attracting a large number of articles whose topics do not meet [[Wikipedia]]'s inclusion standards. Many are quickly deleted, often causing their creators to stop contributing to the site. Authors collect and make available several datasets of deleted articles, heretofore inaccessible, and use them to create a model that can predict with high precision whether or not an article will be deleted. Authors report precision of 98.6% and recall of 97.5% in the best case and high precision with lower, but still useful, recall, in the most difficult case. Authors propose to deploy a system utilizing this model on Wikipedia as a set of decision-support tools to help article creators evaluate and improve their articles before posting, and new article patrollers make more informed decisions about which articles to delete and which to improve. | ||
Revision as of 07:51, 25 January 2021
Authors | Bluma Gelley Torsten Suel |
|---|---|
Publication date | 2013 |
DOI | 10.1145/2491055.2491084 |
Links | Original |
Automated Decision Support for Human Tasks in a Collaborative System: the Case of Deletion in Wikipedia - scientific work related to Wikipedia quality published in 2013, written by Bluma Gelley and Torsten Suel.
Overview
Wikipedia's low barriers to participation have the unintended effect of attracting a large number of articles whose topics do not meet Wikipedia's inclusion standards. Many are quickly deleted, often causing their creators to stop contributing to the site. Authors collect and make available several datasets of deleted articles, heretofore inaccessible, and use them to create a model that can predict with high precision whether or not an article will be deleted. Authors report precision of 98.6% and recall of 97.5% in the best case and high precision with lower, but still useful, recall, in the most difficult case. Authors propose to deploy a system utilizing this model on Wikipedia as a set of decision-support tools to help article creators evaluate and improve their articles before posting, and new article patrollers make more informed decisions about which articles to delete and which to improve.
