Modeling Dynamics of Online Video Popularity

TL;DR

This paper introduces a stochastic fluid model that captures the underlying processes influencing online video popularity dynamics, enabling better prediction and understanding of how videos gain and lose popularity over time.

Contribution

The paper develops a novel stochastic fluid model that incorporates information spreading and user reaction processes to explain video popularity evolution patterns.

Findings

Model accurately matches observed popularity patterns.

Provides insights into factors affecting popularity dynamics.

Enhances prediction capabilities for video popularity trends.

Abstract

Large Internet video delivery systems serve millions of videos to tens of millions of users on daily basis, via Video-on-Demand and live streaming. Video popularity evolves over time. It represents the workload, as welll as business value, of the video to the overall system. The ability to predict video popularity is very helpful for improving service quality and operating efficiency. Previous studies adopted simple models for video popularity, or directly adopted patterns from measurement studies. In this paper, we develop a stochastic fluid model that tries to capture two hidden processes that give rise to different patterns of a given video's popularity evolution: the information spreading process, and the user reaction process. Specifically, these processes model how the video is recommended to the user, the videos inherent attractiveness, and users reaction rate, and yield specific popularity evolution patterns. We then validate our model by matching the predictions of the model with observed patterns from our collaborator, a large content provider in China. This model thus gives us the insight to explain the common and different video popularity evolution patterns and why.