I flicked over a Google research paper last week that I think some of you might find interesting. In the study researchers analyse sharing and its relationship to a video’s popularity, and while the whole paper is worth a read, I found the discussion on the ‘socialness’ of popular videos to be the most interesting.
I’ll post the extract below (Section 6.1 if you’re interested) but the key takeaways from the discussion are:
1) Not all popular videos are highly social
2) Most videos become popular on YouTube through search and related videos (not through sharing/referrals).
3) Viral videos rarely make it into YouTube discovery mechanisms such as search/related videos.
3.1) The data suggests the way YouTube computes related videos does not apply well to viral videos.
Here’s the full extract:
Previous sections of this paper have focused on the full spectrum of YouTube videos. This section focuses on popular videos, which we define to be the top 1% of videos in terms of views. We find that not all popular videos are highly social. The majority of videos become popular through related videos and search.
Figure 11a shows the distribution of the percentage of social views among popular videos in the first 30 days. Note that the distribution is bimodal. That is, it has two peaks, showing that most videos are either viral (peak around 90%) or non-viral (peak around 10%). The peak at 10% is much higher than the one at 90%. If we consider viral videos those with at least 60% of social views, 23% of the videos in this plot are viral. Figure 11b shows the distribution of percentage of views form YouTube search and related videos. This distribution it is still bimodal but it is much more uniform than the previous one, 37% of the videos have at least 60% of their views coming from YouTube search and related.
The bimodal distribution (Fig. 11a, b) means that videos have many views that originate either from YouTube or from external websites/sharing. This pattern can be explained by the fact that viral videos do not seem to make it very often into the YouTube discovery mechanisms such us related videos or YouTube search.
We have a couple of hypotheses to explain that. Related videos rely on co-visitation data1 almost exclusively over a certain period of time. But most viral videos have views in a short period of time and their users are often casual YouTube users (Ulges et al. 2011). These factors may prevent viral videos from making it into the related list of any other videos. On the other hand, videos that make it into the related video list of other videos have a stable source of views; even if it decays, it is sustained for a longer period of time. These hypotheses also suggest that they way we compute related videos today does not apply very well to viral videos.
If you want to read more check out the full paper.