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

该项目旨在了解不准确的消息是如何在公众消费的大规模信息网络上传播的，公众如何回应这种不准确性，以及哪些与内容或元数据相关的特征/特征使某些消息比其他消息更容易出错或更容易出错。该项目的结果有可能帮助建立一个平台，准确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。该项目的技术目标是设计有效的信息提取技术，从类似微博的短消息中适当地提取特征，这些消息通常是嘈杂的。具体而言，它旨在开发各种内容模型，例如基于图的建模，基于情绪的编码，以及基于瓦数和用户频率的指标，以使信息提取技术更能适应现实世界微博平台上常见的噪声和高音量。该项目还包括一个大型真实微博平台的案例研究，以测试所提出方法的有效性及其相对于现有技术的优越性。

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.