课题基金 / 基金详情

CAREER: Transforming Investigative Science and Practice with Expert-Led Crowdsourcing

CAREER: Transforming Investigative Science and Practice with Expert-Led Crowdsourcing
职业:通过专家主导的众包改变调查科学和实践
批准号:
1651969
负责人:
Kurt Luther
金额:
$55.46万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-02-01 至 2023-01-31

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中文摘要
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英文摘要
This research will create an innovative sociotechnical infrastructure that embodies expert-led crowdsourcing and use it as a testbed to conduct a series of mixed-methods studies around three major themes: effectiveness, ethics, and efficiency, in the domains of history, journalism, and national security, respectively. As more information sources have come online, thousands of amateur sleuths collaborate on websites to investigate for themselves. Some crowdsourced investigations have yielded remarkable success, helping to catch criminals and locate missing persons. Other well-intentioned efforts have ended in failure or disaster, and digital vigilantism is on the rise. There is an urgent need for research informed by scientific, technical, and social computing perspectives to shape the future of crowdsourced investigations as a force for societal good. This need can be addressed with expert-led crowdsourcing, a novel approach to investigation that leverages the complementary strengths of experts and crowds to achieve greater results than either could alone. The outreach efforts will bring new tools and techniques to professional and amateur investigators online and in workshops. The educational activities will train diverse groups of students to leverage cutting-edge investigative technologies and big data to answer pressing societal questions.This research will transform the science of investigation in multiple domains by synthesizing concepts and techniques from social computing and crowdsourcing that have mostly been overlooked. For each of the three themes, the research will study expert investigators, extend the infrastructure's support for expert-crowd interaction, and evaluate the benefits of the infrastructure on a series of photo investigation tasks. Rigorous studies of current expert investigations will generate rich findings about current practice and distill design considerations. This research will also introduce the novel approach of expert-led crowdsourcing to overcome the cognitive and resource limits of individual experts and the effectiveness, ethics, and efficiency challenges to crowds. The new platform will make concrete the ideas behind expert-led crowdsourcing and provide a site for evaluation studies that generate foundational knowledge and broader principles of online collaboration and collaborative discovery.
期刊论文(23)
专著(0)
科研奖励(0)
会议论文
Photo Sleuth : Combining Collective Intelligence and Computer Vision to Identify Historical Portraits
照片侦探:结合集体智慧和计算机视觉来识别历史肖像
DOI: --
发表时间: 2018
期刊:
影响因子: --
作者: [V. Mohanty, D. Thames, Kurt Luther]
通讯作者: Kurt Luther
DOI: 10.1145/3491102.3517526
发表时间: 2022
期刊: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
影响因子: --
作者: [Belghith, Yasmine, Venkatagiri, Sukrit, Luther, Kurt]
通讯作者: Luther, Kurt
DOI: 10.1145/3593013.3594080
发表时间: 2023-05
期刊: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency
影响因子: --
作者: [Jacob Thebault-Spieker;Sukrit Venkatagiri;Naomi Mine;Kurt Luther]
通讯作者: Jacob Thebault-Spieker;Sukrit Venkatagiri;Naomi Mine;Kurt Luther
Sedition Hunters: A Quantitative Study of the Crowdsourced Investigation into the 2021 U.S. Capitol Attack
煽动叛乱追捕者:对 2021 年美国国会大厦袭击事件众包调查的定量研究
DOI: 10.1145/3543507.3583514
发表时间: 2023
期刊: Proceedings of the ACM Web Conference 2023
影响因子: --
作者: [Yu, Tianjiao, Venkatagiri, Sukrit, Lourentzou, Ismini, Luther, Kurt]
通讯作者: Luther, Kurt
22
    I-Corps: Historical Photo Identification with Crowdsourcing and Automated Face Recognition
    WORKSHOP: Graduate Student Symposium at the 2017 ACM Conference on Creativity & Cognition
    CHS: Small: Supporting Crowdsourced Sensemaking in Big Data with Dynamic Context Slices
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