DocumentCode
721076
Title
A Bird´s Eye View on Wawacity: Characteristics on Contents and Files
Author
Nan Zhao ; Khoudmi, Soufiane ; Baud, Loic ; Bellot, Patrick
Author_Institution
Telecom ParisTech, Paris, France
fYear
2015
fDate
20-22 April 2015
Firstpage
256
Lastpage
259
Abstract
In recent years content aggregation Internet forums are used more and more by Internet users to locate and get a copy of digital culture goods, which makes them another main consumption place for digital culture goods besides BitTorrent websites. In this paper we give our recent study on a forum called Wawa city to show how these content aggregation Internet forums work, how users behave and what are the characteristics of the digital culture goods distributed on it. A hierarchical forum crawler is created to achieve information collecting from Wawa city. The collected metadata of this forum represent 48 thousand different contents from 10 different categories, 541 thousand different links and 48 million clicks for about 2.5 years. We notice that the content type with the largest thread number is ebooks (43.77%). However, content of series owns the largest number of source links (22.44%) provided by different hosts. Movies resources own the largest click number (25.27%) Internet users. We also find out that there exist less than 10% of total up loaders, called "Big up loaders" that provide more than 80% of source links and represent about 70% of total clicks on Wawa city. Additionally, by using principal component analysis, a big picture of relations is generated to suggest some characteristics clusters on Wawa city.
Keywords
Internet; content management; principal component analysis; Wawacity; content aggregation Internet forums; digital culture goods; ebooks; hierarchical forum crawler; metadata; principal component analysis; Crawlers; Electronic publishing; HDTV; Internet; Motion pictures; Principal component analysis; Streaming media; Content Analysis; Internet User Behaviors; Web Crawling;
fLanguage
English
Publisher
ieee
Conference_Titel
Multimedia Big Data (BigMM), 2015 IEEE International Conference on
Conference_Location
Beijing
Print_ISBN
978-1-4799-8687-3
Type
conf
DOI
10.1109/BigMM.2015.50
Filename
7153890
Link To Document