Abstract
Online crowdfunding platforms such as Kickstarter and Indiegogo make it possible for users to pledge funds to help creators bring their favorite projects into life. With an increasing number of users participating in crowdfunding, researchers are progressively motivated to investigate on improving user experiences by recommending projects and predicting project outcomes. To prompt the sustainable development of these platforms, understanding backers' behaviors becomes also important, as it helps platforms provide better services and improve backer retention. In particular, studying backers' temporal behaviors allows them to monitor the dynamics of backers' actions and develop appropriate strategies in time. Therefore, in this paper, we analyze a large amount of backer data from Kickstarter and Indiegogo, and do a comprehensive quantitative analysis on users' temporal backing patterns. Employing time series clustering methods, we discover four distinct temporal backing patterns on both platforms. In addition, we explore various characteristics of these backing patterns and possible factors affecting backers' behaviors. Finally, we leverage these insights to build a prediction model and show promising results to identify users' backing patterns at a very early stage. The datasets used in this paper are available at: https://go o.gl/ozgLvP.
| Original language | English (US) |
|---|---|
| Title of host publication | WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 369-378 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781450348966 |
| DOIs | |
| State | Published - Jun 25 2017 |
| Event | 9th ACM Web Science Conference, WebSci 2017 - Troy, United States Duration: Jun 25 2017 → Jun 28 2017 |
Publication series
| Name | WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference |
|---|
Other
| Other | 9th ACM Web Science Conference, WebSci 2017 |
|---|---|
| Country | United States |
| City | Troy |
| Period | 6/25/17 → 6/28/17 |
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All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
Cite this
}
Understanding temporal backing paterns in online crowdfunding communities. / Liao, Yiming; Lee, Dongwon; Tran, Thanh; Lee, Kyumin.
WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference. Association for Computing Machinery, Inc, 2017. p. 369-378 (WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Understanding temporal backing paterns in online crowdfunding communities
AU - Liao, Yiming
AU - Lee, Dongwon
AU - Tran, Thanh
AU - Lee, Kyumin
PY - 2017/6/25
Y1 - 2017/6/25
N2 - Online crowdfunding platforms such as Kickstarter and Indiegogo make it possible for users to pledge funds to help creators bring their favorite projects into life. With an increasing number of users participating in crowdfunding, researchers are progressively motivated to investigate on improving user experiences by recommending projects and predicting project outcomes. To prompt the sustainable development of these platforms, understanding backers' behaviors becomes also important, as it helps platforms provide better services and improve backer retention. In particular, studying backers' temporal behaviors allows them to monitor the dynamics of backers' actions and develop appropriate strategies in time. Therefore, in this paper, we analyze a large amount of backer data from Kickstarter and Indiegogo, and do a comprehensive quantitative analysis on users' temporal backing patterns. Employing time series clustering methods, we discover four distinct temporal backing patterns on both platforms. In addition, we explore various characteristics of these backing patterns and possible factors affecting backers' behaviors. Finally, we leverage these insights to build a prediction model and show promising results to identify users' backing patterns at a very early stage. The datasets used in this paper are available at: https://go o.gl/ozgLvP.
AB - Online crowdfunding platforms such as Kickstarter and Indiegogo make it possible for users to pledge funds to help creators bring their favorite projects into life. With an increasing number of users participating in crowdfunding, researchers are progressively motivated to investigate on improving user experiences by recommending projects and predicting project outcomes. To prompt the sustainable development of these platforms, understanding backers' behaviors becomes also important, as it helps platforms provide better services and improve backer retention. In particular, studying backers' temporal behaviors allows them to monitor the dynamics of backers' actions and develop appropriate strategies in time. Therefore, in this paper, we analyze a large amount of backer data from Kickstarter and Indiegogo, and do a comprehensive quantitative analysis on users' temporal backing patterns. Employing time series clustering methods, we discover four distinct temporal backing patterns on both platforms. In addition, we explore various characteristics of these backing patterns and possible factors affecting backers' behaviors. Finally, we leverage these insights to build a prediction model and show promising results to identify users' backing patterns at a very early stage. The datasets used in this paper are available at: https://go o.gl/ozgLvP.
UR - http://www.scopus.com/inward/record.url?scp=85026739552&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85026739552&partnerID=8YFLogxK
U2 - 10.1145/3091478.3091480
DO - 10.1145/3091478.3091480
M3 - Conference contribution
AN - SCOPUS:85026739552
T3 - WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference
SP - 369
EP - 378
BT - WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference
PB - Association for Computing Machinery, Inc
ER -