Speaking Outside the Box: Exploring the Benefits of Unconstrained Input in Crowdsourcing and Citizen Science Platforms

Jon Chamberlain, Udo Kruschwitz, Massimo Poesio

Abstract
Crowdsourcing approaches provide a difficult design challenge for developers. There is a trade-off between the efficiency of the task to be done and the reward given to the user for participating, whether it be altruism, social enhancement, entertainment or money. This paper explores how crowdsourcing and citizen science systems collect data and complete tasks, illustrated by a case study from the online language game-with-a-purpose Phrase Detectives. The game was originally developed to be a constrained interface to prevent player collusion, but subsequently benefited from posthoc analysis of over 76k unconstrained inputs from users. Understanding the interface design and task deconstruction are critical for enabling users to participate in such systems and the paper concludes with a discussion of the idea that social networks can be viewed as form of citizen science platform with both constrained and unconstrained inputs making for a highly complex dataset.
Anthology ID:
2020.cllrd-1.4
Volume:
Proceedings of the LREC 2020 Workshop on "Citizen Linguistics in Language Resource Development"
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
James Fiumara, Christopher Cieri, Mark Liberman, Chris Callison-Burch
Venue:
CLLRD
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
26–34
Language:
English
URL:
https://aclanthology.org/2020.cllrd-1.4
DOI:
Bibkey:
Cite (ACL):
Jon Chamberlain, Udo Kruschwitz, and Massimo Poesio. 2020. Speaking Outside the Box: Exploring the Benefits of Unconstrained Input in Crowdsourcing and Citizen Science Platforms. In Proceedings of the LREC 2020 Workshop on "Citizen Linguistics in Language Resource Development", pages 26–34, Marseille, France. European Language Resources Association.
Cite (Informal):
Speaking Outside the Box: Exploring the Benefits of Unconstrained Input in Crowdsourcing and Citizen Science Platforms (Chamberlain et al., CLLRD 2020)
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