A New Approach to Detecting Content Anomalies in Wikipedia


SİNANÇ TERZİ D. , YAVANOĞLU U.

12th International Conference on Machine Learning and Applications (ICMLA), Florida, Amerika Birleşik Devletleri, 4 - 07 Aralık 2013, ss.288-293 identifier identifier

  • Cilt numarası:
  • Doi Numarası: 10.1109/icmla.2013.137
  • Basıldığı Şehir: Florida
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.288-293

Özet

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.