Difference between revisions of "The Illiterate Editor: Metadata-Driven Revert Detection in Wikipedia"
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+ | {{Infobox work | ||
+ | | title = The Illiterate Editor: Metadata-Driven Revert Detection in Wikipedia | ||
+ | | date = 2013 | ||
+ | | authors = [[Jeffrey Segall]]<br />[[Rachel Greenstadt]] | ||
+ | | doi = 10.1145/2491055.2491066 | ||
+ | | link = https://dl.acm.org/citation.cfm?id=2491055.2491066 | ||
+ | }} | ||
'''The Illiterate Editor: Metadata-Driven Revert Detection in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Jeffrey Segall]] and [[Rachel Greenstadt]]. | '''The Illiterate Editor: Metadata-Driven Revert Detection in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Jeffrey Segall]] and [[Rachel Greenstadt]]. | ||
== Overview == | == Overview == | ||
As the community depends more heavily on [[Wikipedia]] as a source of reliable information, the ability to quickly detect and remove detrimental information becomes increasingly important. The longer incorrect or malicious information lingers in a source perceived as reputable, the more likely that information will be accepted as correct and the greater the loss to source [[reputation]]. Authors present The Illiterate Editor (IllEdit), a content-agnostic, metadata-driven classification approach to Wikipedia revert detection. Authors primary contribution is in building a metadata-based feature set for detecting edit quality, which is then fed into a Support Vector Machine for edit classification. By analyzing edit histories, the IllEdit system builds a profile of user behavior, estimates expertise and spheres of knowledge, and determines whether or not a given edit is likely to be eventually reverted. The success of the system in revert detection (0.844 F-measure) as well as its disjoint feature set as compared to existing, content-analyzing vandalism detection systems, shows promise in the synergistic usage of IllEdit for increasing the [[reliability]] of community information. | As the community depends more heavily on [[Wikipedia]] as a source of reliable information, the ability to quickly detect and remove detrimental information becomes increasingly important. The longer incorrect or malicious information lingers in a source perceived as reputable, the more likely that information will be accepted as correct and the greater the loss to source [[reputation]]. Authors present The Illiterate Editor (IllEdit), a content-agnostic, metadata-driven classification approach to Wikipedia revert detection. Authors primary contribution is in building a metadata-based feature set for detecting edit quality, which is then fed into a Support Vector Machine for edit classification. By analyzing edit histories, the IllEdit system builds a profile of user behavior, estimates expertise and spheres of knowledge, and determines whether or not a given edit is likely to be eventually reverted. The success of the system in revert detection (0.844 F-measure) as well as its disjoint feature set as compared to existing, content-analyzing vandalism detection systems, shows promise in the synergistic usage of IllEdit for increasing the [[reliability]] of community information. |
Revision as of 09:33, 6 January 2020
Authors | Jeffrey Segall Rachel Greenstadt |
---|---|
Publication date | 2013 |
DOI | 10.1145/2491055.2491066 |
Links | Original |
The Illiterate Editor: Metadata-Driven Revert Detection in Wikipedia - scientific work related to Wikipedia quality published in 2013, written by Jeffrey Segall and Rachel Greenstadt.
Overview
As the community depends more heavily on Wikipedia as a source of reliable information, the ability to quickly detect and remove detrimental information becomes increasingly important. The longer incorrect or malicious information lingers in a source perceived as reputable, the more likely that information will be accepted as correct and the greater the loss to source reputation. Authors present The Illiterate Editor (IllEdit), a content-agnostic, metadata-driven classification approach to Wikipedia revert detection. Authors primary contribution is in building a metadata-based feature set for detecting edit quality, which is then fed into a Support Vector Machine for edit classification. By analyzing edit histories, the IllEdit system builds a profile of user behavior, estimates expertise and spheres of knowledge, and determines whether or not a given edit is likely to be eventually reverted. The success of the system in revert detection (0.844 F-measure) as well as its disjoint feature set as compared to existing, content-analyzing vandalism detection systems, shows promise in the synergistic usage of IllEdit for increasing the reliability of community information.