Finding Volunteers' Engagement Profiles in Human Computation for Citizen Science Projects

Lesandro Ponciano, Francisco Brasileiro


Human computation is a computing approach that draws upon human cognitive abilities to solve computational tasks for which there are so far no satisfactory fully automated solutions even when using the most advanced computing technologies available. Human computation for citizen science projects consists in designing systems that allow large crowds of volunteers to contribute to scientific research by executing human computation tasks. Examples of successful projects are Galaxy Zoo and FoldIt. A key feature of this kind of project is its capacity to engage volunteers. An important requirement for the proposal and evaluation of new engagement strategies is having a clear understanding of the typical engagement of the volunteers; however, even though several projects of this kind have already been completed, little is known about this issue. In this paper, we investigate the engagement pattern of the volunteers in their interactions in human computation for citizen science projects, how they differ among themselves in terms of engagement, and how those volunteer engagement features should be taken into account for establishing the engagement encouragement strategies that should be brought into play in a given project. To this end, we define four quantitative engagement metrics to measure different aspects of volunteer engagement, and use data mining algorithms to identify the different volunteer profiles in terms of the engagement metrics. Our study is based on data collected from two projects: Galaxy Zoo and The Milky Way Project. The results show that the volunteers in such projects can be grouped into five distinct engagement profiles that we label as follows: hardworking, spasmodic, persistent, lasting, and moderate. The analysis of these profiles provides a deeper understanding of the nature of volunteers' engagement in human computation for citizen science projects.


Citizen Science; Human Computation; Engagement; Participation; Retention

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Abdi, H. (2010). Coefficient of variation. Encyclopedia of Research Design., 169–171.

Anonymized authors. (2014). Anonymized title. Anonymized journal.

Bakker, A. B., & Demerouti, E. (2008). Towards a model of work engagement. Career development international, 13(3), 209–223.

Clary, E. G., Snyder, M., Ridge, R. D., Copeland, J., Stukas, A. A., Haugen, J., & Miene, P. (1998). Understanding and assessing the motivations of volunteers: a functional approach. Journal of personality and social psychology, 74(6), 1516.

Cooper, S., Khatib, F., Treuille, A., Barbero, J., Lee, J., Beenen, M., . . . others (2010). Predicting protein structures with a multiplayer online game. Nature, 466(7307), 756–760.

Cravens, J. (2000). Virtual volunteering: Online volunteers providing assistance to human service agencies. Journal of Technology in Human Services, 17(2-3), 119–136.

Eisner, D., Grimm Jr, R. T., Maynard, S., & Washburn, S. (2009). The new volunteer workforce. Stanford Social Innovation Review, 3, 31–37.

Fortson, L., Masters, K., Nichol, R., Borne, K., Edmondson, E., Lintott, C., . . . Wallin, J. (2012). Galaxy zoo: Morphological classification and citizen science. In Advances in machine learning and data mining for astronomy (p. 213-236).

Geiger, R. S., & Halfaker, A. (2013). Using edit sessions to measure participation in wikipedia. In Proceedings of the 2013 conference on computer supported cooperative work (pp. 861–870).

Gonza lez-Roma , V., Schaufeli, W. B., Bakker, A. B., & Lloret, S. (2006). Burnout and work engagement: Independent factors or opposite poles? Journal of Vocational Behavior, 68(1), 165–174.

Hager, M. A. (2004). Volunteer management practices and retention of volunteers.

Hothorn, T., Hornik, K., & Zeileis, A. (2006). Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical Statistics, 15(3), 651–674.

Jennett, C., Blandford, A., Brohan, P., & Cox, A. (2014). Designing for dabblers and deterring drop-outs in citizen science. In Proceedings of the acm 2014 conference on human factors in computing system.

Kraut, R. E., Resnick, P., Kiesler, S., Burke, M., Chen, Y., Kittur, N., . . . Riedl, J. (2012). Building successful online communities: Evidence-based social design. Mit Press.

Lehmann, J., Lalmas, M., Yom-Tov, E., & Dupret, G. (2012). Models of user engagement. In Proceedings of the 20th international conference on user modeling, adaptation, and personalization (pp. 164–175). Berlin, Heidelberg: Springer-Verlag.

Lintott, C., & Reed, J. (2013). Human computation in citizen science. In Handbook of human computation (pp. 153–162). Springer.

Lintott, C. J., Schawinski, K., Slosar, A., Land, K., Bamford, S., Thomas, D., . . . Vandenberg, J. (2008, Sep). Galaxy zoo: morphologies derived from visual inspection of galaxies from the sloan digital sky survey. Monthly Notices of the Royal Astronomical Society, 389.

Lopez, C., Farzan, R., & Brusilovsky, P. (2012). Personalized incremental users’ engagement: driving o contributions one step forward. In Proceedings of the 17th acm international conference on supporting group work (pp. 189–198).

Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., & Hornik, K. (2014). cluster: Cluster analysis basics and extensions [Computer software manual]. (R package version 1.15.2 — For new features, see the ’Changelog’ file (in the package source))

Mao, A., Kamar, E., & Horvitz, E. (2013). Why stop now? predicting worker engagement in online crowdsourcing. In Proceedings of the first aaai conference on human computation and crowdsourcing.

Maslach, C., & Jackson, S. E. (1981). The measurement of experienced burnout. Journal of Organizational Behavior, 2(2), 99–113.

McCay-Peet, L., Lalmas, M., & Navalpakkam, V. (2012). On saliency, affect and focused attention. In Proceedings of the sigchi conference on human factors in computing systems (pp. 541–550). New York, NY, USA: ACM.

Mehrzadi, D., & Feitelson, D. G. (2012). On extracting session data from activity logs. In Proceedings of the 5th annual international systems and storage conference (p. 3).

Mendez, B. J., Craig, N., & Westphal, A. J. (2005). Stardust@home: Enlisting students and the public in the search for interstellar dust. American Astronomical Society Meeting 207, Bulletin of the American Astronomical Society, 37, 1265.

Millen, D. R., & Patterson, J. F. (2002). Stimulating social engagement in a community network. In Proceedings of the 2002 acm conference on computer supported cooperative work (pp. 306–313).

O’Brien, H. L., & Toms, E. G. (2008). What is user engagement? a conceptual framework for defining user engagement with technology. Journal of the American Society for Information Science and Technology, 59(6), 938–955.

O’Brien, H. L., & Toms, E. G. (2010). The development and evaluation of a survey to measure user engagement. Journal of the American Society for Information Science and Technology, 61(1), 50–69.

Quinn, A. J., & Bederson, B. B. (2011). Human computation: a survey and taxonomy of a growing field. In CHI (pp. 1403–1412).

Raddick, J., Bracey, G., Gay, P. L., Lintott, C. J., Murray, P., Schawinski, K., . . . Vandenberg, J. (2010). Galaxy zoo: Exploring the motivations of citizen science volunteers. Astronomy Education Review, 9(1), 010103.

Raddick, J., Lintott, C., Bamford, S., Land, K., Locksmith, D., Murray, P., . . . Andreescu, D. (2008). Galaxy zoo: Motivations of citizen scientists. In Bulletin of the american astronomical society (Vol. 40, p. 240).

Rotman, D., Preece, J., Hammock, J., Procita, K., Hansen, D., Parr, C., . . . Jacobs, D. (2012). Dynamic changes in motivation in collaborative citizen-science projects. In Proceedings of the acm 2012 conference on computer supported cooperative work (pp. 217–226).

Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20, 53–65.

Sanchez, C. A., Blomer, J., Buncic, P., Chen, G., Ellis, J., Quintas, D. G., . . . Yadav, R. (2011). Volunteer clouds and citizen cyberscience for lhc physics. In Journal of physics: Conference series (Vol. 331, p. 062022).

Simpson, M. R. (2009). Engagement at work: A review of the literature. International Journal of Nursing Studies, 46(7), 1012–1024.

Simpson, R., Povich, M., Kendrew, S., Lintott, C., Bressert, E., Arvidsson, K., . . . others (2012). The milky way project first data release: a bubblier galactic disc. Monthly Notices of the Royal Astronomical Society, 424(4), 2442–2460.

Wilson, J. (2000). Volunteering. Annual review of sociology, 26(1), 215–240.

Zhang, A., & Lu, Q. (2002). The regulation of self-efficacy and attributional feedback on motivation. Social Behavior and Personality: an international journal, 30(3), 281–287.



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