CAREER: Mining Reliable Information from Crowdsourced Data

职业：从众包数据中挖掘可靠信息

• 批准号: 2226108
• 负责人: Jing Gao
• 金额: $ 50.06万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2226108&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

随着移动设备和社交媒体平台的普及，任何人都可以随时随地发布对任何活动、事件或物体的观察结果。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。这样的系统可以极大地提高运输、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，即个人用户可能提供不可靠甚至误导性的信息。该项目确定了在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，并追求一个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，该项目解决了信息准确性问题，这将使众包数据无处不在但准确性可能受到怀疑的许多应用受益。特别地，本项目结合众包的各种特性，开发了挖掘可靠信息的新方法：1)众包平台收集用户对特定对象的观察。利用其他有价值的信息源，如时空、用户影响和文本数据，从这些观测中有效地检测可靠信息。2)设计有效的隐私保护和预算分配机制，以更好地激励积极的众包。这些调查与所提出方法的理论和实践方面的探索相结合。从理论角度出发，探讨了估计可靠性的置信度和所提出方法的收敛性等基本问题。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，从而对这些领域产生新的见解。除了研究进展之外，本项目还有助于教育创新，因为所提出的方法被应用于同伴评估和问答等教育方法。关于这个项目的更多信息，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