  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.
