Engagement and Popularity Dynamics of YouTube Videos and Sensitivity to Meta-Data

被引:39
作者
Hoiles, William [1 ]
Aprem, Anup [1 ]
Krishnamurthy, Vikram [2 ,3 ]
机构
[1] Univ British Columbia, Dept Elect & Comp Engn, Vancouver, BC V6T 1Z4, Canada
[2] Cornell Univ, Dept Elect & Comp Engn, Ithaca, NY 14850 USA
[3] Cornell Univ, Cornell Tech, Ithaca, NY 14850 USA
关键词
YouTube; social media; sensitivity analysis; metadata; user engagement; channel dynamics; popularity prediction; Granger causality; machine learning; EXTREME LEARNING-MACHINE; VARIABLE SELECTION;
D O I
10.1109/TKDE.2017.2682858
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
YouTube, with millions of content creators, has become the preferred destination for viewing videos online. Through the Partner program, YouTube allows content creators to monetize their popular videos. Of significant importance for content creators is which meta-level features (title, tag, thumbnail, and description) are most sensitive for promoting video popularity. The popularity of videos also depends on the social dynamics, i.e., the interaction of the content creators (or channels) with YouTube users. Using real-world data consisting of about 6 million videos spread over 25 thousand channels, we empirically examine the sensitivity of YouTube meta-level features and social dynamics. The key meta-level features that impact the view counts of a video include: first day view count, number of subscribers, contrast of the video thumbnail, Google hits, number of keywords, video category, title length, and number of upper-case letters in the title, respectively, and illustrate that these meta-level features can be used to estimate the popularity of a video. In addition, optimizing the meta-level features after a video is posted increases the popularity of videos. In the context of social dynamics, we discover that there is a causal relationship between views to a channel and the associated number of subscribers. Additionally, insights into the effects of scheduling and video playthrough in a channel are also provided. Our findings provide a useful understanding of user engagement in YouTube.
引用
收藏
页码:1426 / 1437
页数:12
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