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

• 批准号: 1838147
• 负责人: Arjun Mukherjee
• 金额: $ 30.6万
• 依托单位: University of Houston
• 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 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1838147&HistoricalAwards=false
• 关键词: BIGDATA; Collaborative; Research; Collective; Mining

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.

很大一部分互联网社交媒体内容存在于新闻媒体主办的数千个专业社区中，通常以读者论坛或新闻文章评论的形式出现。据说此类网站的用户形成了一个垂直社交社区 (VSC)，因为他们与单一媒体源深度互动。 虽然与 Facebook 等广泛的社区相比，每个 VSC 都很小，但它们很重要，因为它们揭示了社会不同阶层对各种世界事件的看法。这对于下游情报和预测分析来说是非常有用的资源。 然而，当前的网络爬虫无法有效访问VSC。因此，他们的数据对搜索引擎来说是不可见的，并且对分析工具来说仍然是隐藏的。 该项目的目标是有效访问在线新闻报道中凝聚的垂直社交社区，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进行分析的工具。 该项目的教育部分包括研究生和本科生培训和研究的参与，以及将研究项目和成果纳入课程。研究人员将开发算法来挖掘数千个垂直社交社区生成的内容，并使数据管理和分析工具可以透明地访问其内容。研究人员将开发用于内容检测的新型深度学习技术，并构建一种新型可扩展的端到端系统，用于实时访问和集体挖掘这些社区，能够根据不断变化的想法处理大型并行数据流。具体算法将包括用户群体估计、用于自动抓取内容的引导通信模式，以及用于情报和预测分析的细粒度情绪分析。软件工具将提供给学术界和工业界的研究人员。用于实施所开发技术的免费开源软件的分发将增强现有的研究基础设施。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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

Pro/Con: Neural Detection of Stance in Argumentative Opinion

赞成/反对：论证性意见中立场的神经检测

• 发表时间: 2019
• 期刊: Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation
• 作者: Hosseinia, M.

On the Usefulness of Personality Traits in Opinion-oriented Tasks

• DOI: 10.26615/978-954-452-072-4_062
• 发表时间: 2021
• 作者: Marjan Hosseinia;E. Dragut;Dainis Boumber;Arjun Mukherjee

Claim Verification under Positive Unlabeled Learning

• DOI: 10.1109/asonam49781.2020.9381336
• 发表时间: 2020-12
• 期刊: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
• 作者: Fan Yang;E. Dragut;Arjun Mukherjee

A Domain-Independent Holistic Approach to Deception Detection

• DOI: 10.26615/978-954-452-072-4_147
• 发表时间: 2021-11
• 作者: Sadat Shahriar;Arjun Mukherjee;O. Gnawali

Predicting Personal Opinion on Future Events with Fingerprints

• DOI: 10.18653/v1/2020.coling-main.162
• 发表时间: 2020-12
• 期刊: 2022 2nd International Conference on Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI)
• 作者: Fan Yang;Eduard Constantin Dragut;Arjun Mukherjee