GroundTruth: Augmenting Expert Image Geolocation with Crowdsourcing and Shared Representations
GroundTruth: Augmenting Expert Image Geolocation with Crowdsourcing and Shared Representations
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DOI：
10.1145/3359209
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发表时间：
2019-11
影响因子：
--
通讯作者：
Sukrit Venkatagiri;Jacob Thebault-Spieker;Rachel Kohler;John Purviance;Rifat Sabbir Mansur;Kurt Luther
中科院分区：
文献类型：
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作者：
Sukrit Venkatagiri;Jacob Thebault-Spieker;Rachel Kohler;John Purviance;Rifat Sabbir Mansur;Kurt Luther
专家调查人员带来了先进的技能和丰富的经验来分析视觉证据，但他们面临着时间和注意力的限制。相比之下，新手群体可以高度可扩展和并行化，但缺乏专业知识。在本文中，我们介绍了共享表示的概念，人群增强专家工作，专注于复杂的感觉制作任务的图像地理定位的专业记者和人权调查员。我们建立了GroundTruth，这是一个在线系统，它使用三种共享的表示法--图表、网格和热图--让专家们能够在真实的时间内与人群一起工作，对图像进行地理定位。我们对11名专家和567名群众工作者进行了混合方法评估，发现GroundTruth帮助专家对图像进行地理定位，并揭示了专家与群众互动的挑战和成功策略。我们还讨论了为视觉搜索、意义建构等设计共享表示。
Expert investigators bring advanced skills and deep experience to analyze visual evidence, but they face limits on their time and attention. In contrast, crowds of novices can be highly scalable and parallelizable, but lack expertise. In this paper, we introduce the concept of shared representations for crowd--augmented expert work, focusing on the complex sensemaking task of image geolocation performed by professional journalists and human rights investigators. We built GroundTruth, an online system that uses three shared representations-a diagram, grid, and heatmap-to allow experts to work with crowds in real time to geolocate images. Our mixed-methods evaluation with 11 experts and 567 crowd workers found that GroundTruth helped experts geolocate images, and revealed challenges and success strategies for expert-crowd interaction. We also discuss designing shared representations for visual search, sensemaking, and beyond.