Understanding temporal backing paterns in online crowdfunding communities

Yiming Liao, Dongwon Lee, Thanh Tran, Kyumin Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publicationWebSci 2017 - Proceedings of the 2017 ACM Web Science Conference
PublisherAssociation for Computing Machinery, Inc
Pages369-378
Number of pages10
ISBN (Electronic)9781450348966
DOIs
StatePublished - Jun 25 2017
Event9th ACM Web Science Conference, WebSci 2017 - Troy, United States
Duration: Jun 25 2017Jun 28 2017

Publication series

NameWebSci 2017 - Proceedings of the 2017 ACM Web Science Conference

Other

Other9th ACM Web Science Conference, WebSci 2017
CountryUnited States
CityTroy
Period6/25/176/28/17

Fingerprint

Sustainable development
Time series
Chemical analysis

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Cite this

Liao, Y., Lee, D., Tran, T., & Lee, K. (2017). Understanding temporal backing paterns in online crowdfunding communities. In WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference (pp. 369-378). (WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/3091478.3091480
Liao, Yiming ; Lee, Dongwon ; Tran, Thanh ; Lee, Kyumin. / Understanding temporal backing paterns in online crowdfunding communities. WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference. Association for Computing Machinery, Inc, 2017. pp. 369-378 (WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference).
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Liao, Y, Lee, D, Tran, T & Lee, K 2017, Understanding temporal backing paterns in online crowdfunding communities. in WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference. WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference, Association for Computing Machinery, Inc, pp. 369-378, 9th ACM Web Science Conference, WebSci 2017, Troy, United States, 6/25/17. https://doi.org/10.1145/3091478.3091480

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 proceedingConference contribution

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Liao Y, Lee D, Tran T, Lee K. Understanding temporal backing paterns in online crowdfunding communities. In 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). https://doi.org/10.1145/3091478.3091480