Intervention strategies for increasing engagement in crowdsourcing: Platform, predictions, and experiments

Avi Segal, Ya'akov Kobi Gal, Ece Kamar, Eric Horvitz, Alex Bowyer, Grant Miller

Research output: Contribution to journalConference articlepeer-review

13 Scopus citations


Volunteer-based crowdsourcing depend critically on maintaining the engagement of participants. We explore a methodology for extending engagement in citizen science by combining machine learning with intervention design. We first present a platform for using real-time predictions about forthcoming disengagement to guide interventions. Then we discuss a set of experiments with delivering different messages to users based on the proximity to the predicted time of disengagement. The messages address motivational factors that were found in prior studies to influence users' engagements. We evaluate this approach on Galaxy Zoo, one of the largest citizen science application on the web, where we traced the behavior and contributions of thousands of users who received intervention messages over a period of a few months. We found sensitivity of the amount of user contributions to both the timing and nature of the message. Specifically, we found that a message emphasizing the helpfulness of individual users significantly increased users' contributions when delivered according to predicted times of disengagement, but not when delivered at random times. The influence of the message on users' contributions was more pronounced as additional user data was collected and made available to the classifier.

Original languageEnglish
Pages (from-to)3861-3867
Number of pages7
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - 1 Jan 2016
Event25th International Joint Conference on Artificial Intelligence, IJCAI 2016 - New York, United States
Duration: 9 Jul 201615 Jul 2016

ASJC Scopus subject areas

  • Artificial Intelligence


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