Sampath Kannan

From WikiPapers
Jump to: navigation, search

Sampath Kannan is an author.

Publications

Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
STiki: An Anti-Vandalism Tool for Wikipedia Using Spatio-Temporal Analysis of Revision Metadata Wikipedia
Collaboration software
Information security
Intelligent routing
Spatio-temporal processing
WikiSym English July 2010 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-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 classiffcation (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. 0 0
Detecting Wikipedia Vandalism via Spatio-Temporal Analysis of Revision Metadata Wikipedia
Spatio-temporal reputation
Vandalism
Collaboration software
Content-based access control
EUROSEC English April 2010 Blatantly unproductive edits undermine the quality of the collaboratively-edited encyclopedia, Wikipedia. They not only disseminate dishonest and offensive content, but force editors to waste time undoing such acts of vandalism. Language-processing has been applied to combat these malicious edits, but as with email spam, these filters are evadable and computationally complex. Meanwhile, recent research has shown spatial and temporal features effective in mitigating email spam, while being lightweight and robust. In this paper, we leverage the spatio-temporal properties of revision metadata to detect vandalism on Wikipedia. An administrative form of reversion called rollback enables the tagging of malicious edits, which are contrasted with nonoffending edits in numerous dimensions. Crucially, none of these features require inspection of the article or revision text. Ultimately, a classifier is produced which flags vandalism at performance comparable to the natural-language efforts we intend to complement (85% accuracy at 50% recall). The classifier is scalable (processing 100+ edits a second) and has been used to locate over 5,000 manually-confirmed incidents of vandalism outside our labeled set. 9 3
STiki: An anti-vandalism tool for wikipedia using spatio-temporal analysis of revision metadata WikiSym 2010 English 2010 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-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). Our demonstration will provide an introduction to spatio-temporal properties, demonstrate the STiki software, and discuss alternative research uses for the open-source code. 0 0
Spatio-temporal analysis of Wikipedia metadata and the STiki anti-vandalism tool WikiSym English 2010 0 0
Spatio-temporal analysis of wikipedia metadata and the STiki anti-vandalism tool WikiSym 2010 English 2010 The bulk of Wikipedia anti-vandalism tools require natural language processing over the article or diff text. However, our prior work demonstrated the feasibility of using spatio-temporal properties to locate malicious edits. STiki is a real-time, on-Wikipedia tool leveraging this technique. The associated poster reviews STiki's methodology and performance. We find competing anti-vandalism tools inhibit maximal performance. However, the tool proves particularly adept at mitigating long-term embedded vandalism. Further, its robust and language-independent nature make it well-suited for use in less-patrolled Wiki installations. 0 0