User Donations in a Crowdsourced Video System

TL;DR

This paper introduces a new dataset on user donations in BiliBili, analyzes donation dynamics and social factors, and develops machine learning models to predict donation destinations, enhancing understanding of user engagement in crowdsourced video platforms.

Contribution

It provides the first publicly available dataset on user donations in a crowdsourced video system and applies machine learning to predict donation behaviors.

Findings

Donation dynamics are influenced by social relationships.

Machine learning models can accurately predict donation destinations.

Factors impacting donations include social ties and user activity levels.

Abstract

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.