Title :
Predicting the Popularity of Trending Arabic Wikipedia Articles Based on External Stimulants Using Data/Text Mining Techniques
Author :
Al-Mutairi, Hanadi Muqbil ; Khan, Mohammad Badruddin
Author_Institution :
Coll. of Comput. & Inf. Sci., Al-Imam Univ., Saudi Arabia
Abstract :
Wikipedia is considered to be one of the most famous online encyclopedias. We study the issues related to trending articles on Arabic Wikipedia and how it is influenced by certain external stimulants: for example, breaking news, celebrities´ tweets, special events from the past, instant messages on any social media application or any other reasons that could affect the Arabic Wikipedia articles in terms of the number of visitors, which we named the popularity level. By using a data- and text- mining techniques, and the software platform Rapidminer, we developed two models that enabled us to predict the popularity level of Arabic articles on Wikipedia, depending on the features of their stimulants.
Keywords :
Web sites; data mining; encyclopaedias; natural language processing; text analysis; Arabic Wikipedia article; Rapidminer; data mining technique; external stimulant; online encyclopedia; popularity level; software platform; text mining technique; Classification algorithms; Electronic publishing; Encyclopedias; Internet; Text categorization;
Conference_Titel :
Cloud Computing (ICCC), 2015 International Conference on
Conference_Location :
Riyadh
Print_ISBN :
978-1-4673-6617-5
DOI :
10.1109/CLOUDCOMP.2015.7149651
