Topic
Crowdsourcing
About: Crowdsourcing is a research topic. Over the lifetime, 12889 publications have been published within this topic receiving 230638 citations.
Papers published on a yearly basis
Papers
More filters
Posted Content•
TL;DR: In this article, a case study of a crowdsourced law-reform process in Finland is presented, where the public is invited to contribute to the law reform process by sharing their knowledge and ideas for a better policy.
Abstract: This article reports a pioneering case study of a crowdsourced law-reform process in Finland. In the crowdsourcing experiment, the public was invited to contribute to the law-reform process by sharing their knowledge and ideas for a better policy. This article introduces a normative design framework of five principles for crowdsourced policymaking: inclusiveness, accountability, transparency, modularity, and synthesis. Inclusiveness, accountability, and transparency are overarching principles for crowdsourced policymaking. Modularity and synthesis support these overarching principles and are instrumental in achieving the main goals of crowdsourced policymaking, namely, an efficient search for knowledge and democratic deliberation among the participants. These principles apply to both the design of the process and the medium that the process takes place in, i.e., the technology facilitating crowdsourcing. This article analyzes the design of the crowdsourced off-road traffic law experiment in Finland using the five principles described above and provides a future research agenda for examining design aspects in crowdsourced policymaking.
69 citations
TL;DR: In this article, the authors explore the convergence between recent research in urban sustainability governance and crowdsourcing and highlight the upcoming trends in participatory research and policy-making that exploit ICT and Web 2.0 social software.
69 citations
TL;DR: High quality eyewitnesses of rainfall and flooding events are retrieved from social media by applying deep learning approaches on user generated texts and photos, and events are detected through spatiotemporal clustering and visualized together with these high qualityewitnesses in a web map application.
Abstract: In recent years, pluvial floods caused by extreme rainfall events have occurred frequently. Especially in urban areas, they lead to serious damages and endanger the citizens’ safety. Therefore, real-time information about such events is desirable. With the increasing popularity of social media platforms, such as Twitter or Instagram, information provided by voluntary users becomes a valuable source for emergency response. Many applications have been built for disaster detection and flood mapping using crowdsourcing. Most of the applications so far have merely used keyword filtering or classical language processing methods to identify disaster relevant documents based on user generated texts. As the reliability of social media information is often under criticism, the precision of information retrieval plays a significant role for further analyses. Thus, in this paper, high quality eyewitnesses of rainfall and flooding events are retrieved from social media by applying deep learning approaches on user generated texts and photos. Subsequently, events are detected through spatiotemporal clustering and visualized together with these high quality eyewitnesses in a web map application. Analyses and case studies are conducted during flooding events in Paris, London and Berlin.
69 citations
26 Apr 2014
TL;DR: A platform that combines learning and crowdsourcing to benefit both the workers and the requesters is described, which found that by using the system workers gained new skills and produced high-quality edits for requested images, even if they had little prior experience editing images.
Abstract: Crowdsourcing complex creative tasks remains difficult, in part because these tasks require skilled workers. Most crowdsourcing platforms do not help workers acquire the skills necessary to accomplish complex creative tasks. In this paper, we describe a platform that combines learning and crowdsourcing to benefit both the workers and the requesters. Workers gain new skills through interactive step-by-step tutorials and test their knowledge by improving real-world images submitted by requesters. In a series of three deployments spanning two years, we varied the design of our platform to enhance the learning experience and improve the quality of the crowd work. We tested our approach in the context of LevelUp for Photoshop, which teaches people how to do basic photograph improvement tasks using Adobe Photoshop. We found that by using our system workers gained new skills and produced high-quality edits for requested images, even if they had little prior experience editing images.
69 citations
TL;DR: It is shown that the total wasted effort is at most the maximum effort which implies that crowdsourcing contests are a 2-approximation to an idealized model of conventional procurement.
68 citations