History: STiki: An Anti-Vandalism Tool for Wikipedia using Spatio-Temporal Analysis of Revision Metadata
Source of version: 2 (current)
!STiki: An Anti-Vandalism Tool for Wikipedia using Spatio-Temporal Analysis of Revision Metadata
__Track__: Demos
__Authors__: Andrew West, Sampath Kannan and Insup Lee
__Slides__: [http://www.cis.upenn.edu/~westand/docs/slides_wikisym_demo.pdf|SLIDES]
__Abstract__
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 our prior work, which found they perform comparably to NLP-e orts 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 de nitive classi cation (and if necessary, reversion on Wikipedia). Our demonstration will provide an introduction to spatio-temporal properties, demonstrate the STiki software, and discuss alternative research uses for the open-source code.
__Track__: Demos
__Authors__: Andrew West, Sampath Kannan and Insup Lee
__Slides__: [http://www.cis.upenn.edu/~westand/docs/slides_wikisym_demo.pdf|SLIDES]
__Abstract__
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 our prior work, which found they perform comparably to NLP-e orts 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 de nitive classi cation (and if necessary, reversion on Wikipedia). Our demonstration will provide an introduction to spatio-temporal properties, demonstrate the STiki software, and discuss alternative research uses for the open-source code.