@article {9999,
	title = {Appealing to different motivations in a message to recruit citizen scientists: results of a field experiment},
	journal = {Journal of Science Communication},
	volume = {17},
	year = {2018},
	chapter = {A02},
	abstract = {<p>This study examines the relative efficacy of citizen science recruitment messages appealing to four motivations that were derived from previous research on motives for participation in citizen-science projects. We report on an experiment (N=36,513) that compared the response to email messages designed to appeal to these four motives for participation. We found that the messages appealing to the possibility of contributing to science and learning about science attracted more attention than did one about helping scientists but that one about helping scientists generated more initial contributions. Overall, the message about contributing to science resulted in the largest volume of contributions and joining a community, the lowest. The results should be informative to those managing citizen-science projects.</p>},
	keywords = {Citizen Science},
	doi = {10.22323/2.17010202},
	url = {https://jcom.sissa.it/archive/17/01/JCOM_1701_2018_A02},
	attachments = {https://crowston.syr.edu/sites/crowston.syr.edu/files/JCOM_1701_2018_A02.pdf},
	author = {Lee, Tae Kyoung and Kevin Crowston and Mahboobeh Harandi and Carsten {\O}sterlund and Grant Miller}
}
@proceedings {9999,
	title = {Blending machine and human learning processes},
	year = {2017},
	abstract = {<p>Citizen science projects rely on contributions from volunteers to achieve their scientific goals and so face a dilemma: providing volunteers with explicit training might increase the quality of contributions, but at the cost of losing the work done by newcomers during the training period, which for many is the only work they will contribute to the project. Based on research in cognitive science on how humans learn to classify images, we have designed an approach to use machine learning to guide the presentation of tasks to newcomers that help them more quickly learn how to do the image classification task while still contributing to the work of the project. A Bayesian model for tracking this learning is presented.</p>},
	doi = {10.24251/HICSS.2017.009},
	url = {http://hdl.handle.net/10125/41159},
	attachments = {https://crowston.syr.edu/sites/crowston.syr.edu/files/training\%20v3\%20to\%20share_0.pdf},
	author = {Kevin Crowston and Carsten {\O}sterlund and Lee, Tae Kyoung}
}
@article {2017,
	title = {Recruiting messages matter: Message strategies to attract citizen scientists},
	year = {2017},
	type = {Poster},
	address = {Portland, OR},
	abstract = {Although participation of citizen scientists is critical for a success of citizen science projects (a distinctive form of crowdsourcing), little attention has been paid to what types of messages can effectively recruit citizen scientists. Derived from previous studies on citizen scientists{\textquoteright} motivations, we created and sent participants one of four recruiting messages for a new project, Gravity Spy, appealing to different motivations (i.e., learning about science, social proof, contribution to science, and altruism). Counter to earlier studies on motivation, our results showed that messages appealing to learning, contribution and social proof were more effective than a message appealing to altruism. We discuss the inconsistency between the present and prior study results and plans for future work.},
	doi = {10.1145/3022198.3026335},
	attachments = {https://crowston.syr.edu/sites/crowston.syr.edu/files/cpa143-leeA.pdf},
	author = {Lee, Tae Kyoung and Kevin Crowston and Carsten {\O}sterlund and Grant Miller}
}
