Catching a Viral Video
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 YouTube by analyzing sharing and its relationship to video popularity using 1.5 million YouTube videos. The socialness 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 viewership patterns of highly social videos is very different 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.
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 viewership patterns of highly social videos is very different 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.
Research Areas
