Difference between revisions of "Stiki: an Anti-Vandalism Tool for Wikipedia Using Spatio-Temporal Analysis of Revision Metadata"
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+ | {{Infobox work | ||
+ | | title = Stiki: an Anti-Vandalism Tool for Wikipedia Using Spatio-Temporal Analysis of Revision Metadata | ||
+ | | date = 2010 | ||
+ | | authors = [[Andrew G. West]]<br />[[Sampath Kannan]]<br />[[Insup Lee]] | ||
+ | | doi = 10.1145/1832772.1832814 | ||
+ | | link = https://dl.acm.org/citation.cfm?doid=1832772.1832814 | ||
+ | }} | ||
'''Stiki: an Anti-Vandalism Tool for Wikipedia Using Spatio-Temporal Analysis of Revision Metadata''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Andrew G. West]], [[Sampath Kannan]] and [[Insup Lee]]. | '''Stiki: an Anti-Vandalism Tool for Wikipedia Using Spatio-Temporal Analysis of Revision Metadata''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Andrew G. West]], [[Sampath Kannan]] and [[Insup Lee]]. | ||
== Overview == | == Overview == | ||
STiki is an anti-vandalism tool for [[Wikipedia]]. Unlike similar tools, STiki does not rely on [[natural language processing]] (NLP) over the article or diff text to locate vandalism. Instead, STiki leverages spatio-temporal properties of revision metadata. The feasibility of utilizing such properties was demonstrated in prior work, which found they perform comparably to NLP-efforts while being more efficient, robust to evasion, and language independent. STiki is a real-time, on-Wikipedia implementation based on these properties. It consists of, (1) a server-side processing engine that examines revisions, scoring the likelihood each is vandalism, and, (2) a client-side GUI that presents likely vandalism to end-users for definitive classification (and if necessary, reversion on Wikipedia). Authors demonstration will provide an introduction to spatio-temporal properties, demonstrate the STiki software, and discuss alternative research uses for the [[open-source]] code. | STiki is an anti-vandalism tool for [[Wikipedia]]. Unlike similar tools, STiki does not rely on [[natural language processing]] (NLP) over the article or diff text to locate vandalism. Instead, STiki leverages spatio-temporal properties of revision metadata. The feasibility of utilizing such properties was demonstrated in prior work, which found they perform comparably to NLP-efforts while being more efficient, robust to evasion, and language independent. STiki is a real-time, on-Wikipedia implementation based on these properties. It consists of, (1) a server-side processing engine that examines revisions, scoring the likelihood each is vandalism, and, (2) a client-side GUI that presents likely vandalism to end-users for definitive classification (and if necessary, reversion on Wikipedia). Authors demonstration will provide an introduction to spatio-temporal properties, demonstrate the STiki software, and discuss alternative research uses for the [[open-source]] code. |
Revision as of 10:33, 16 September 2019
Authors | Andrew G. West Sampath Kannan Insup Lee |
---|---|
Publication date | 2010 |
DOI | 10.1145/1832772.1832814 |
Links | Original |
Stiki: an Anti-Vandalism Tool for Wikipedia Using Spatio-Temporal Analysis of Revision Metadata - scientific work related to Wikipedia quality published in 2010, written by Andrew G. West, Sampath Kannan and Insup Lee.
Overview
STiki is an anti-vandalism tool for Wikipedia. Unlike similar tools, STiki does not rely on natural language processing (NLP) over the article or diff text to locate vandalism. Instead, STiki leverages spatio-temporal properties of revision metadata. The feasibility of utilizing such properties was demonstrated in prior work, which found they perform comparably to NLP-efforts while being more efficient, robust to evasion, and language independent. STiki is a real-time, on-Wikipedia implementation based on these properties. It consists of, (1) a server-side processing engine that examines revisions, scoring the likelihood each is vandalism, and, (2) a client-side GUI that presents likely vandalism to end-users for definitive classification (and if necessary, reversion on Wikipedia). Authors demonstration will provide an introduction to spatio-temporal properties, demonstrate the STiki software, and discuss alternative research uses for the open-source code.