|Title||Reaching Consensus in Crowdsourced Transcription of Biocollections Information|
|Year of Publication||2014|
|Authors||Matsunaga, Andréa, Mast Austin, and Fortes José A. B.|
|Keywords||Citizen Science, crowdsourcing|
|Abstract||Crowdsourcing can be a cost-effective method for tackling the problem of digitizing historical biocollections data, and a number of crowdsourcing platforms have been developed to facilitate interaction with the public and to design simple “Human Intelligence Tasks”. However, the problem of reaching consensus on the response of the crowd is still challenging for tasks for which a simple majority vote is inadequate. This paper (a) describes the challenges faced when trying to reach consensus on data transcribed by different workers, (b) offers consensus algorithms for textual data and a consensus-based controller to assign a dynamic number of workers per task, and (c) proposes further enhancements of future crowdsourcing tasks in order to minimize the need for complex consensus algorithms. Experiments using the proposed algorithms show up to a 45-fold increase in ability to reach consensus when compared to majority voting using exact string matching. In addition, the controller is able to decrease the crowdsourcing cost by 55% when compared to a strategy that uses a fixed number of workers.|
Andréa Matsunaga, Austin Mast, and José Fortes recently published on the proceedings of the 10th IEEE International conference on e-Science. The article, Reaching Consensus in Crowdsourced Transcription of Biocollections Information, shows methods for reaching consensus from crowdsourcing output for transcribing collections labels. This is the presentation Andréa Matsunaga gave during the conference.