Tom Broxton el al., Catching a viral video, J Intell Inf Syst 2011

Citation

Tom Broxton and Yannet Interian and Jon Vaver and Mirjam Wattenhofer: Catching a viral video. Journal of Intelligent Information Systems 2011: 1-19.

Online version

[1]

Summary

This is a paper of Google Research introducing the preliminary analysis on virus video [2](Viral Video Analysis). The data set used in the study is a large-scale, confidential and exclusive data set, the revealed conclusion from which are considerable valuable. Specifically it

Different research reaches the same conclusion that the most distinguishing characteristic of the viral video is its lifespan. Compared with "popular videos" which are capable of attracting large number of views, the viral video gain traction in social media quickly and fade quickly as well.

Data set

1.5 million video randomly selected from the sey of video uploaded to YouTube between Apirl 2009 and March 2010. Each video is associated with the meta-data including its category, the number of view at daily level and most important the "referrer" (It seems impossible for obtain such information outside of Google) which accounts the source from which the user came to watch a particular video. The authors further classify the referrer into social and non-social categories:

Social: External link and embeds (from a social site such as Facebook, blogs or instant messages) and Unknown (the user typed or copied URL into browser)

Non-social: Youtube internal link (related or recommended videos) and Youtube search(found by an search engine).

Conclusions

First of all, the authors categorize the videos into 10 group according to their level of "socialness" which is quantified by the fraction of social views during the first 30 days of viewing. The least social group has 0.0 to 6.1% social views whereas the most social group enjoys 81.8 to 100% social views (the number of videos in each group is approximately the same).

Notes

[3] Support website

[4] J. Leskovec, M. McGlohon, C. Faloutsos, N. Glance, M. Hurst. Cascading behavior in large blog graphs.SDM’07.

[5] X. Wang and A. McCallum. Topics over time: a non-markov continuous-time model of topical trends.Proc. KDD, 2006.

[6] X. Wang, C. Zhai, X. Hu, R. Sproat. Mining correlated bursty topic patterns from coordinated text streams.KDD, 2007.