Analyzing and Predicting User Donations in Social Live Video Streaming

Abstract

Social live video streaming has been a major Internet phenomenon with the rise of platforms like Facebook-Live, Youtube-Live, and Twich. New features tailored by these platforms provide unprecedented opportunities for viewers to engage and for broadcasters to gain tangible rewards through user donation. In this article, we conduct an in-depth analysis of user donations, a distinctive feature and prominent phenomenon in social live streaming, which however we have very limited understandings with. Based on a publicly available (anonymized) dataset with detailed information on over 4 million donation relationships that cover over 2 million users and worth in total over 200 million US dollars, we quantitatively reveal donation disparity and dynamics of donation relationships. Among other results, we find that repeated donation relationships largely exist and the strength significantly increases with the repetition level. Finally, we adopt machine-learned classifiers to accurately predict future donations. Our findings shed lights on the user retention problem and the design of social live video streaming services.