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

本项目旨在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，公众如何对这种不准确做出反应，以及与内容或元数据相关的特征/功能使某些信息比其他信息更容易出错。 该项目的成果有可能帮助建立一个平台，准确地识别错误正在传播的信息网络，并有效地管理/控制这样的错误propagation.The技术目标的这个项目涉及到有效的信息提取技术，适当地提取功能的微博类消息，是短，往往嘈杂的设计。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瓦片和用户频率的度量，以使信息提取技术对真实世界微博平台上通常存在的噪声和高音量更具弹性。 该项目还包括一个大规模的真实世界的微博平台上的案例研究，以测试所提出的方法的有效性和他们的优势，现有的技术。

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.