Abstract: 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.

Citation: Avi Segal, Ya’akov Gal, Ece Kamar, Eric Horvitz, Alex Bower, Grant Miller. Intervention Strategies for Increasing Engagement in Volunteer-Based Crowdsourcing. International Joint Conference on Artificial Intelligence (IJCAI), New York, USA, July 2016.

Download: http://bit.ly/2jh9fgG

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: The majority of volunteers participating in citizen science projects perform only a few tasks each before leaving the system. We designed an intervention strategy to reduce disengagement in 16 different citizen science projects. Targeted users who had left the system received emails that directly addressed motivational factors that affect their engagement. Results show that participants receiving the emails were significantly more likely to return to productive activity when compared to a control group.

Keywords: Peer production, crowdsourcing, citizen science, intervention strategies.

Citation: Segal, A., Gal, Y.A.K., Simpson, R.J., Victoria Homsy, V., Hartswood, M., Page, K.R. and Jirotka, M., 2015, May. Improving productivity in citizen science through controlled intervention. In Proceedings of the 24th International Conference on World Wide Web Companion (pp. 331-337). International World Wide Web Conferences Steering Committee.

Download: http://bit.ly/1SiqBku