BIGDATA: F: Collaborative Research: Collective Mining of Vertical Social Communities

BIGDATA：F：协同研究：垂直社交社区的集体挖掘

基本信息

批准号：
1838145
负责人：
Eduard Dragut
金额：
$ 42.79万
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：
2018-09-15 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1838145&HistoricalAwards=false
关键词：
BIGDATA Collaborative Research Collective Mining

项目摘要

A large fraction of internet social media content is found in thousands of specialized communities that are hosted by news outlets, typically in the form of reader forums or comments on news articles. The users of the such a site are said to form a vertical social community (VSC), because they deeply engage with a single media source. While each VSC is tiny compared to broad communities such as Facebook, they are important because they expose how different segments of society feel about various world events. This can be a very useful resource for downstream intelligence and predictive analytics. However, current web crawlers cannot effectively access VSCs. Thus their data is invisible to search engines, and remains hidden from analytics tools. The goals of this project are to enable effective access to vertical social communities coalesced at news reports online, and to mine their comments and debates. This project will provide researchers with tools to collect data from these communities and analyze them. The educational component of the project includes the involvement of graduate and undergraduate student training and research and the incorporation of research projects and results in courses.The researchers will develop algorithms to unearth the content generated at thousands of vertical social communities and make their content transparently accessible to data management and analytics tools. The researchers will develop novel deep learning techniques for content detection, and build a novel scalable end-to-end system for real-time access and collective mining of these communities, capable of handling large parallel data streams based on shifting ideas. The specific algorithms will include user population estimation, bootstrap communication patterns for automatic crawling of content, and fine-grained sentiment analysis for intelligence and predictive analytics. Software tools will be made available to researchers in academe and industry. Distribution of free, open-source software for implementing the techniques developed will 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.

在数千个由新闻媒体主持的专业社区中发现了很大一部分的互联网社交媒体内容，通常是以读者论坛的形式或新闻文章的评论。据说该网站的用户形成了垂直社会社区（VSC），因为它们与单个媒体来源深入互动。尽管与Facebook等广泛社区相比，每个VSC都很小，但它们很重要，因为它们揭示了社会对各种世界活动的不同感受。对于下游智能和预测分析，这可能是非常有用的资源。但是，当前的Web爬网无法有效访问VSC。因此，它们的数据对于搜索引擎是看不见的，并且仍然隐藏在分析工具中。该项目的目标是使在线新闻报道中合并的垂直社会社区有效访问，并挖掘他们的评论和辩论。该项目将为研究人员提供从这些社区收集数据并分析它们的工具。该项目的教育组成部分包括研究生和本科生培训和研究的参与以及研究项目的合并和课程的合并。研究人员将开发算法，以发掘成千上万个垂直社会社区生成的内容，并使他们的内容可透明地访问用于数据管理和分析工具。研究人员将开发出新颖的深度学习技术来进行内容检测，并建立一个新颖的可扩展端到端系统，以实时访问和这些社区的集体采矿，能够根据变化的想法处理大型平行数据流。特定的算法将包括用户人口估计，自动爬行内容的引导性通信模式以及用于智能和预测分析的细粒情感分析。软件工具将提供给学术界和行业的研究人员。分发免费的开源软件用于实施开发的技术将增强现有的研究基础架构。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。