Name
Abstract | ||
|---|---|---|
User generated video systems like YouTube and Twitch.tv have been a major internet phenomenon. They have attracted a vast user base with their many and varied contents provided by their users, and a series of social features tailored for online viewing. In hoping for building a more lively community and encouraging the content creators to share more, recently many such systems have introduced crowdsourcing mechanisms wherein creators get tangible rewards through user donations. User donation is a very special form of user relationships. It influences user engagement in the community, and has a great impact on the success of these systems. However, user donations and donation relationships remain trade secrets for most enterprises and to date are still unexplored. It is not clear at what scale are the donations or how users donate in these systems. In this work, we attempt to fill this gap. We obtain and provide a publicly available dataset on user donations in BiliBili, a popular user generated video system in China with 76.4 million average monthly active users. Based on detailed information on over 5 million videos, over 700 thousand content creators, and over 1.5 million user donations, we quantitatively reveal the characteristics of user donations, we examine their correlations with the upload behavior and content popularity of the creators, and we adopt machine-learned classifiers to accurately predict the creators who will receive donations and who will donate in the future.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3308560.3316702 | Companion Proceedings of The 2019 World Wide Web Conference |
Keywords | Field | DocType |
User Generated Content (UGC), User donation, user generated video systems | Donation,World Wide Web,Computer science,Crowdsourcing,Upload,User engagement,User generated video,Content popularity,The Internet | Conference |
ISBN | Citations | PageRank |
978-1-4503-6675-5 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adele Lu Jia | 1 | 64 | 8.01 |
| Xiaoxue Shen | 2 | 0 | 0.68 |
| Siqi Shen | 3 | 135 | 14.47 |
| Yongquan Fu | 4 | 36 | 11.32 |
| Liwen Peng | 5 | 1 | 0.69 |