A New Approach to Detecting Content Anomalies in Wikipedia

Author

Sinanc, Duygu ; Yavanoglu, Uraz

Author_Institution

Dept. of Comput., Gazi Univ., Ankara, Turkey

Volume

2

fYear

2013

fDate

4-7 Dec. 2013

Firstpage

288

Lastpage

293

Abstract

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.

Keywords

Internet; Web sites; botany; data mining; hypermedia markup languages; neural nets; pattern classification; text analysis; text editing; zoology; ANN; English Wikipedia; HTML contents; OES; Web mining techniques; anomaly rate; artificial neural network; botany pages; content anomaly detection; encyclopedia; open editing schemas; politics; terrorism; text classification; train phase; wrong categorization; zoology pages; Artificial neural networks; Electronic publishing; Encyclopedias; Internet; Web pages; artificial neural networks; class mapping; data mining; open editing schemas; web classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Applications (ICMLA), 2013 12th International Conference on

Conference_Location

Miami, FL

Type

conf

DOI

10.1109/ICMLA.2013.137

Filename

6786122