CAREER: Mining Reliable Information from Crowdsourced Data

职业：从众包数据中挖掘可靠信息

• 批准号: 1553411
• 负责人: Jing Gao
• 金额: $ 50.06万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1553411&HistoricalAwards=false
• 关键词: CAREER; Mining; Reliable; Information; Crowdsourced

With the proliferation of mobile devices and social media platforms, any person can publicize observations about any activity, event or object anywhere and at any time. The confluence of these enormous crowdsourced data can contribute to an inexpensive, sustainable and large-scale decision system that has never been possible before. Such a system could vastly improve the efficiency and cost of transportation, healthcare, and many other applications. The main obstacle in building such a system lies in the problem of information veracity, i.e., individual users might provide unreliable or even misleading information. This project identifies important research questions in the task of mining reliable information from noisy and unreliable crowdsourced data, and pursues an integrated research and education plan to address these questions. Through integrating data from various sources, this project addresses information veracity, which will benefit the many applications where crowdsourced data are ubiquitous but veracity can be suspect.In particular, this project develops novel methods to mine reliable information by taking into consideration various properties of crowdsourcing: 1) Crowdsourcing platforms collect users' observations about certain objects. Other valuable information sources, such as spatial-temporal, user influence, and textual data, are leveraged to effectively detect reliable information from these observations. 2) Effective privacy protection and budget allocation mechanisms are designed to better motivate active crowdsourcing. These investigations are integrated with the exploration of both theoretical and practical aspects of the proposed methods. From the theoretical perspective, fundamental questions regarding the confidence in the estimated reliability and the convergence of the proposed methods are explored. From the practical perspective, the proposed methods are adapted to tackle challenging problems in various applications such as transportation, healthcare and education to enable new insights into these domains. In addition to the research advances, this project contributes to educational innovation, as the proposed methods are applied to educational methodologies such as peer assessment and question answering. Additional information about this project, including research results, publications, datasets, and software, can be found at http://www.cse.buffalo.edu/~jing/crowd.htm

随着移动的设备和社交媒体平台的普及，任何人都可以随时随地公布对任何活动、事件或物体的观察。这些庞大的众包数据的融合可以促成一个廉价、可持续和大规模的决策系统，这在以前是不可能的。这样的系统可以大大提高运输，医疗保健和许多其他应用的效率和成本。 建立这样一个系统的主要障碍在于信息的准确性问题，即，个别用户可能提供不可靠或甚至误导性的信息。 该项目确定了从嘈杂和不可靠的众包数据中挖掘可靠信息的任务中的重要研究问题，并寻求综合研究和教育计划来解决这些问题。 通过整合来自不同来源的数据，该项目解决了信息的准确性问题，这将有益于众包数据无处不在但准确性可能受到怀疑的许多应用。特别是，该项目考虑到众包的各种特性，开发了挖掘可靠信息的新方法：1）众包平台收集用户对某些对象的观察。其他有价值的信息源，如时空，用户的影响，和文本数据，被利用来有效地从这些观察检测可靠的信息。2)有效的隐私保护和预算分配机制旨在更好地激励积极的众包。这些调查与所提出的方法的理论和实践方面的探索相结合。从理论的角度来看，基本问题的置信度估计的可靠性和所提出的方法的收敛性进行了探讨。从实践的角度来看，所提出的方法适用于解决各种应用中的挑战性问题，如交通，医疗保健和教育，以实现对这些领域的新见解。 除了研究进展外，该项目还有助于教育创新，因为所提出的方法适用于教育方法，如同行评估和问答。 有关该项目的其他信息，包括研究结果、出版物、数据集和软件，可以在http://www.cse.buffalo.edu/~jing/crowd.htm上找到。