Analyzing the dynamics of online video popularity

doi:10.1016/S1005-8885(17)60212-7

JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM ›› 2017, Vol. 24 ›› Issue (3): 58-69.doi: 10.1016/S1005-8885(17)60212-7

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Analyzing the dynamics of online video popularity

Received:2016-10-21 Revised:2017-06-27 Online:2017-06-30 Published:2017-06-30
Supported by:
111 Project of China

Abstract

Abstract: Given the large volume of video content and the diversity of user attention, it is of great importance to understand the characteristics of online video popularity for technological, economic and social reasons. In this paper, based on the data collected from a leading online video service provider in China, namely Youku, the dynamics of online video popularity are analyzed in-depth from four key aspects: overall popularity distribution, individual popularity distribution, popularity evolution pattern and early-future popularity relationship. How the popularity of a set of newly upload videos distributes throughout the observation period is first studied. Then the notion of active days is proposed, and the per-day and per-hour popularity distributions of individual videos are carefully studied. Next, how the popularity of an individual video evolves over time is investigated. The evolution patterns are further defined according to the number and temporal locations of popularity bursts, in order to describe the popularity growth trend. At last, the linear relationship between early video popularity and future video popularity are examined on a log-log scale. The relationship is found to be largely impacted by the popularity evolution patterns. Therefore, the specialized models are proposed to describe the correlation according to the popularity evolution patterns. Experiment results show that specialized models can better fit the correlation than a general model. Above all, the analysis results in our work can provide direct help in practical for the interested parties of online video service such as service providers, online advisers, and network operators.

Key words: online video service, online content popularity, popularity evolution pattern, early-future popularity relationship

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Analyzing the dynamics of online video popularity

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