Stochastic dynamics and the predictability of big hits in online videos

TL;DR

This paper analyzes YouTube view data to understand the origin and predictability of big hits, proposing a stochastic model with Lévy noise that improves hit probability estimation over traditional models.

Contribution

It introduces a stochastic differential equation with Lévy noise to better model video view dynamics and predict big hits, surpassing traditional proportional-growth models.

Findings

Average view gain is proportional to current views, consistent with rich-get-richer mechanisms.

View fluctuations are heavy-tailed, affecting hit predictability.

The proposed Lévy noise model better estimates big hit probabilities.

Abstract

The competition for the attention of users is a central element of the Internet. Crucial issues are the origin and predictability of big hits, the few items that capture a big portion of the total attention. We address these issues analyzing 10 million time series of videos' views from YouTube. We find that the average gain of views is linearly proportional to the number of views a video already has, in agreement with usual rich-get-richer mechanisms and Gibrat's law, but this fails to explain the prevalence of big hits. The reason is that the fluctuations around the average views are themselves heavy tailed. Based on these empirical observations, we propose a stochastic differential equation with L\'evy noise as a model of the dynamics of videos. We show how this model is substantially better in estimating the probability of an ordinary item becoming a big hit, which is considerably underestimated in the traditional proportional-growth models.