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.

该项目试图了解如何在公众消费的大规模信息网络上传播不准确的消息，公众如何应对这种不准确性以及与其他内容的内容或元数据相关的特征/功能使某些信息更具错误或错误或错误的特征。 该项目的结果有可能帮助构建一个平台，该平台可以准确地识别在信息网络上传播的错误并有效地管理/控制此类错误传播。该项目的技术目标涉及设计有效的信息提取技术的设计，这些技术从微博式消息中正确提取功能，这些信息类似于微博的信息，这些信息很短，而且通常是愚蠢的。具体而言，它旨在开发各种内容模型，例如基于图形的建模，基于情感的编码以及基于木板和用户频率的指标，以使信息提取技术更适合于现实世界中的微型微博平台上通常存在的噪声和大量量。 该项目还包括对大型现实世界微博平台进行的案例研究，以测试拟议方法的有效性及其优越性。

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