EAGER: Assessing Influence of News Articles on Emerging Events

EAGER：评估新闻文章对新兴事件的影响

• 批准号: 1842183
• 负责人: Zoran Obradovic
• 金额: $ 30万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1842183&HistoricalAwards=false
• 关键词: EAGER; Assessing; Influence; News; Articles

There is growing interest in mining social media streams for early detection of (important) events, like crisis detection (and response) and predicting social unrest. Social media and news articles play an important role in documenting daily societal events. News outlets host social media platforms that facilitate users to engage in debating daily news topics. For example, the social networks at NY Times, The Guardian, and Washington Post have more than 130,000 users each. Together, they constitute a considerable segment of the varied opinions of society at large. The objective of this project is to assess the feasibility of leveraging the trend of past social response to news articles observed over a few hundred social media streams to detect the emergence of new important social, economic, and political events. The project benefits multiple segments of society, such as social scientists and policy makers, because the results of the proposed project provide tools to predict important real-life events using indicators observed on social media. The educational component of the project includes the involvement of graduate and undergraduate students' training and research and the incorporation of research projects and results in appropriate courses.The difficult and high risk problem addressed in this project is that of transforming the streams of social media chatter at hundreds of news outlets into data signals from which to mine those signals foretelling the imminence of an (important) event, and to develop sound predictive analytics on top of those signals. This project seeks creating a proof of concept that works with a few hundred social communities from news outlets. Specific aims consist of (i) developing methods for automatic data collection and (ii) efficient predictive modeling at that scale. The results (e.g., software tools) are made available to benefit researchers in academia and industry. Free, open-source software for implementing the developed techniques will be distributed to enhance existing research infrastructure.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人们越来越有兴趣挖掘社交媒体流，以早期检测（重要）事件，如危机检测（和响应）和预测社会动荡。 社交媒体和新闻文章在记录日常社会事件方面发挥着重要作用。新闻媒体主办社交媒体平台，方便用户参与辩论每日新闻话题。例如，《纽约时报》、《卫报》和《华盛顿邮报》的社交网络都有超过13万用户。它们共同构成了整个社会各种意见的相当大的一部分。该项目的目标是评估利用过去社会对数百个社交媒体流中观察到的新闻文章的反应趋势来检测新的重要社会，经济和政治事件的出现的可行性。该项目使社会科学家和政策制定者等多个社会阶层受益，因为拟议项目的结果提供了使用社交媒体上观察到的指标预测重要现实生活事件的工具。该项目的教育部分包括研究生和本科生参与培训和研究，并将研究项目和结果纳入适当的课程。该项目解决的困难和高风险问题是将数百个新闻媒体的社交媒体聊天流转化为数据信号，从中挖掘这些信号，预测一场灾难的即将到来。（重要）事件，并在这些信号之上开发合理的预测分析。该项目旨在创建一个概念验证，与来自新闻媒体的数百个社交社区合作。具体目标包括（i）开发自动数据收集方法和（ii）在该规模上进行有效的预测建模。结果（例如，软件工具），使学术界和工业界的研究人员受益。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Temporal Graph Regression via Structure-Aware Intrinsic Representation Learning

• DOI: 10.1137/1.9781611975673.41
• 发表时间: 2019
• 作者: Chao Han;X. Cao;Marija Stanojevic;Mohamed F. Ghalwash;Z. Obradovic

Cosine similarity for multiplex network summarization

多重网络摘要的余弦相似度

• DOI: 10.1145/3487351.3488331
• 发表时间: 2021
• 期刊: ASONAM '21: Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
• 作者: Polychronopoulou, Athanasia;Zhou, Fang;Obradovic, Zoran
• 通讯作者: Obradovic, Zoran

Time-to-event estimation by re-defining time

通过重新定义时间来估计事件发生时间

• DOI: 10.1016/j.jbi.2019.103326
• 发表时间: 2019
• 期刊: Journal of Biomedical Informatics
• 影响因子: 4.5
• 作者: Cao, Xi Hang;Han, Chao;Glass, Lucas M.;Kindman, Allen;Obradovic, Zoran
• 通讯作者: Obradovic, Zoran

Distinguishability of graphs: a case for quantum-inspired measures

• DOI: 10.1145/3487351.3488330
• 发表时间: 2021-11
• 期刊: Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
• 作者: A. Polychronopoulou;Jumanah Alshehri;Z. Obradovic

A Distributable Convex Approach for Graph Structure Discovery

图结构发现的分布式凸方法

• 发表时间: 2019
• 期刊: 15th International Workshop on Mining and Learning with Graphs (MLG
• 作者: Han, C.