Print Email Facebook Twitter Conversational crowdsourcing Title Conversational crowdsourcing Author Qiu, S. (TU Delft Web Information Systems) Gadiraju, Ujwal (TU Delft Web Information Systems) Bozzon, A. (TU Delft Web Information Systems; TU Delft Human-Centred Artificial Intelligence) Houben, G.J.P.M. (TU Delft Web Information Systems) Contributor Ustalov, Dmitry (editor) Casati, Fabio (editor) , Alexey (editor) Baidakova, Daria (editor) Date 2020 Abstract The trend of remote work leads to the prosperity of crowdsourcing marketplaces. In crowdsourcing marketplaces, online workers can select their preferable tasks and then complete them to get paid, while requesters design and publish tasks to acquire their desirable data. The standard user interface of the crowdsourcing task is the web page, where users provide answers using HTML-based web elements, and the task-related information (including instructions and questions) is displayed on a single web page. Although the traditional way of presenting tasks is straightforward, it could negatively affect workers’ satisfaction and performance by causing problems such as boredom and fatigue. To address this challenge, we proposed a novel concept — conversational crowdsourcing, which employs conversational interfaces to facilitate crowdsourcing task execution. With conversational crowdsourcing, workers receive task information as messages from a conversational agent, and provide answers by sending messages back to the agent. In this vision paper, we introduce our recent work in terms of using conversational crowdsourcing to improve worker performance and experience by employing novel human-computer interaction affordances. Our findings reveal that conversational crowdsourcing has important implications in improving the worker satisfaction and requester-worker relationship in crowdsourcing marketplaces. To reference this document use: http://resolver.tudelft.nl/uuid:279e55c7-29b9-4082-944f-d94499a07ba1 Source Proceedings of the Crowd Science Workshop: Remoteness, Fairness, and Mechanisms as Challenges of Data Supply by Humans for Automation co-located with 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 2736 Event 2020 Crowd Science Workshop: Remoteness, Fairness, and Mechanisms as Challenges of Data Supply by Humans for Automation, 2020-12-11, Vancouver, Canada Series CEUR Workshop Proceedings, 1613-0073 Part of collection Institutional Repository Document type conference paper Rights © 2020 S. Qiu, Ujwal Gadiraju, A. Bozzon, G.J.P.M. Houben Files PDF paper1.pdf 469.47 KB Close viewer /islandora/object/uuid:279e55c7-29b9-4082-944f-d94499a07ba1/datastream/OBJ/view