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A new approach to detecting content anomalies in Wikipedia
Abstract The rapid growth of the web has caused to The rapid growth of the web has caused to availability of data effective if its content is well organized. Despite the fact that Wikipedia is the biggest encyclopedia on the web, its quality is suspect due to its Open Editing Schemas (OES). In this study, zoology and botany pages are selected in English Wikipedia and their html contents are converted to text then Artificial Neural Network (ANN) is used for classification to prevent disinformation or misinformation. After the train phase, some irrelevant words added in the content about politics or terrorism in proportion to the size of the text. By the time unsuitable content is added in a page until the moderators' intervention, the proposed system realized the error via wrong categorization. The results have shown that, when words number 2% of the content is added anomaly rate begins to cross the 50% border.omaly rate begins to cross the 50% border.
Abstractsub The rapid growth of the web has caused to The rapid growth of the web has caused to availability of data effective if its content is well organized. Despite the fact that Wikipedia is the biggest encyclopedia on the web, its quality is suspect due to its Open Editing Schemas (OES). In this study, zoology and botany pages are selected in English Wikipedia and their html contents are converted to text then Artificial Neural Network (ANN) is used for classification to prevent disinformation or misinformation. After the train phase, some irrelevant words added in the content about politics or terrorism in proportion to the size of the text. By the time unsuitable content is added in a page until the moderators' intervention, the proposed system realized the error via wrong categorization. The results have shown that, when words number 2% of the content is added anomaly rate begins to cross the 50% border.omaly rate begins to cross the 50% border.
Bibtextype inproceedings  +
Doi 10.1109/ICMLA.2013.137  +
Has author Sinanc D. + , Yavanoglu U. +
Has extra keyword Data mining + , Learning systems + , Neural networks + , New approaches + , Open editing schemas + , Rapid growth + , Web classification + , Wikipedia + , Text processing +
Has keyword Artificial neural networks + , Class mapping + , Data mining + , Open editing schemas + , Web classification +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 288–293  +
Published in Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013 +
Title A new approach to detecting content anomalies in Wikipedia +
Type conference paper  +
Volume 2  +
Year 2013 +
Creation dateThis property is a special property in this wiki. 6 November 2014 11:46:50  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 6 November 2014 11:46:50  +
DateThis property is a special property in this wiki. 2013  +
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