User Donations in a Crowdsourced Video System
This work addresses a gap in understanding user engagement and financial dynamics in online video communities, which is incremental as it provides foundational data and methods for a previously unexplored area.
The study tackled the lack of public data on user donations in crowdsourced video systems by collecting and analyzing a dataset from BiliBili, revealing factors influencing donations and using machine learning to predict donation destinations with timeliness and accuracy.
Crowdsourced video systems like YouTube and Twitch.tv have been a major internet phenomenon and are nowadays entertaining over a billion users. In addition to video sharing and viewing, over the years they have developed new features to boost the community engagement and some managed to attract users to donate, to the community as well as to other users. User donation directly reflects and influences user engagement in the community, and has a great impact on the success of such systems. Nevertheless, user donations in crowdsourced video systems remain trade secrets for most companies and to date are still unexplored. In this work, we attempt to fill this gap, and we obtain and provide a publicly available dataset on user donations in one crowdsourced video system named BiliBili. Based on information on nearly 40 thousand donators, we examine the dynamics of user donations and their social relationships, we quantitively reveal the factors that potentially impact user donation, and we adopt machine-learned classifiers and network representation learning models to timely and accurately predict the destinations of the majority and the individual donations.