DocumentCode :
2191499
Title :
Catching a Viral Video
Author :
Broxton, Tom ; Interian, Yannet ; Vaver, Jon ; Wattenhofer, Mirjam
fYear :
2010
fDate :
13-13 Dec. 2010
Firstpage :
296
Lastpage :
304
Abstract :
The sharing and re-sharing of videos on social sites, blogs e-mail, and other means has given rise to the phenomenon of viral videos - videos that become popular through internet sharing. In this paper we seek to better understand viral videos on You Tube by analyzing sharing and its relationship to video popularity using 1.5 million You Tube videos. The social ness of a video is quantified by classifying the referrer sources for video views as social (e.g. an emailed link) or non-social (e.g. a link from related videos). By segmenting videos according to their fraction of social views, we find that highly social videos behave differently than less social videos. For example, the highly social videos rise to, and fall from, their peak popularity more quickly than less social videos. We also find that not all highly social videos become popular, and not all popular videos are highly social. And, despite their ability to generate large volumes of views over a short period of time, only 21% of the most popular videos (in terms of 30-day views) can be classified as viral. The observations made here lay the ground work for future work related to the creation of classification and predictive models for online videos.
Keywords :
Internet; social networking (online); YouTube; social sites; viral video; YouTube; viral videos;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining Workshops (ICDMW), 2010 IEEE International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
978-1-4244-9244-2
Electronic_ISBN :
978-0-7695-4257-7
Type :
conf
DOI :
10.1109/ICDMW.2010.160
Filename :
5693313
Link To Document :
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