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dc.contributor.authorTummalapenta, Raga Sowmya
dc.date.accessioned2014-03-04T21:00:14Z
dc.date.available2014-03-04T21:00:14Z
dc.date.issued2012-12
dc.identifier.othertummalapenta_raga-sowmya_201212_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/tummalapenta_raga-sowmya_201212_ms
dc.identifier.urihttp://hdl.handle.net/10724/28618
dc.description.abstractThe collaborative model of Wikipedia is simple and open. This nature of Wikipedia challenges its trustworthiness, leading to vandalism. There are several current vandalism detection techniques but none of them focus on detecting elusive vandalism. This type do not contain normal characteristics of vandalism and hence difficult to detect. We have proposed multicontext aware detection techniques for determining whether an elusive edit is vandalized or not. The main idea of these techniques is to check whether an edit lies within the context of other words within a particular Wikipedia article. For the experimental purposes, we make use of a PAN corpus, which is a large collection of Wikipedia edits. Then we perform a feature extraction followed by a data trained classification using WEKA. Accuracy of our methods is calculated using f1-measure. Results show that the context aware techniques are efficient since they result in highly less number of false positives and negatives.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectWikipedia, Vandalism, Elusive Vandalism, WEKA, context, Search Engine, Accuracy
dc.titleA context aware approach for detecting elusive vandalism in Wikipedia
dc.typeThesis
dc.description.degreeMS
dc.description.departmentComputer Science
dc.description.majorComputer Science
dc.description.advisorLakshmish Ramaswamy
dc.description.committeeLakshmish Ramaswamy
dc.description.committeeKhaled Rasheed
dc.description.committeeKang Li


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