EAGER: An Investigation of the Propagation of Error-Resistant and Error-Prone Messages Over Large-Scale Information Networks

EAGER：大规模信息网络上防错和易错消息传播的研究

• 批准号: 1651475
• 负责人: Raghav Rao
• 金额: $ 20万
• 依托单位: University of Texas at San Antonio
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1651475&HistoricalAwards=false
• 关键词: EAGER; Investigation; Propagation; Error; Resistant

This project seeks to understand how inaccurate messages are propagated over large-scale information networks that are consumed by the general public, how the public responds to such inaccuracy, and what content- or metadata-related characteristics/features make certain messages more error-resistant or error-prone than others. The results of the project have the potential to help build a platform that accurately identifies errors being propagated on an information network and effectively manages/controls such error propagation.The technical objective of this project involves the design of efficient information extraction techniques that properly extract features from microblog-like messages that are short and often noisy. Specifically, it aims to develop a variety of content models, e.g., graph-based modeling, sentiment-based coding, and shingle- and user-frequency based metrics to make the information extraction techniques more resilient to the noise and high volume commonly present on real-world microblog platforms. The project also includes a case study over a large-scale real-world microblog platform to test the effectiveness of the proposed approaches and their superiority over the existing techniques.

这个项目试图了解不准确的信息是如何在公众消费的大规模信息网络上传播的，公众对这种不准确的信息是如何反应的，以及哪些内容或元数据相关的特征/特征使某些信息比其他信息更耐错或更容易出错。该项目的成果有可能帮助建立一个平台，准确地识别在信息网络上传播的错误，并有效地管理/控制这种错误传播。该项目的技术目标包括设计有效的信息提取技术，从简短且经常有噪音的类似微博的消息中正确地提取特征。具体地说，它的目标是开发各种内容模型，例如基于图的建模、基于情感的编码、基于瓦片和用户频率的度量，以使信息提取技术对现实世界微博平台上常见的噪声和大容量具有更强的弹性。该项目还包括一个大型真实微博平台上的案例研究，以测试所提出的方法的有效性及其相对于现有技术的优势。

项目成果

S-Isomap++: Multi manifold learning from streaming data

• DOI: 10.1109/bigdata.2017.8257987
• 发表时间: 2017-10
• 期刊: 2017 IEEE International Conference on Big Data (Big Data)
• 作者: Suchismit Mahapatra;V. Chandola

ICT mediated rumor beliefs and resulting user actions during a community crisis

社区危机期间，信息通信技术介导谣言信念和由此产生的用户行为

• DOI: 10.1016/j.giq.2018.03.006
• 发表时间: 2018
• 期刊: Government Information Quarterly
• 影响因子: 7.8
• 作者: Oh, Onook;Gupta, Priya;Agrawal, Manish;Raghav Rao, H.
• 通讯作者: Raghav Rao, H.

An Investigation of Misinformation Harms Related to Social Media During Humanitarian Crises

对人道主义危机期间社交媒体相关错误信息危害的调查

• DOI: 10.1007/978-981-15-3817-9_10
• 发表时间: 2020
• 期刊: Secure Knowledge Management In Artificial Intelligence Era.
• 作者: Tran T., Valecha R.

Cyber-rumor sharing under a homeland security threat in the context of government Internet surveillance: The case of South-North Korea conflict

• DOI: 10.1016/j.giq.2017.04.002
• 发表时间: 2017-04-01
• 期刊: GOVERNMENT INFORMATION QUARTERLY
• 影响因子: 7.8
• 作者: Kwon, K. Hazel;Rao, H. Raghav
• 通讯作者: Rao, H. Raghav

A system for intergroup prejudice detection: The case of microblogging under terrorist attacks

• DOI: 10.1016/j.dss.2018.06.003
• 发表时间: 2018-09-01
• 期刊: DECISION SUPPORT SYSTEMS
• 影响因子: 7.5
• 作者: Dutta, Haimonti;Kwon, K. Hazel;Rao, H. Raghav
• 通讯作者: Rao, H. Raghav