Collaborative Research: SAI-R: Integrative Cyberinfrastructure for Enhancing and Accelerating Online Abuse Research

合作研究：SAI-R：用于加强和加速在线滥用研究的综合网络基础设施

• 批准号: 2228616
• 负责人: Long Cheng
• 金额: $ 37.5万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2228616&HistoricalAwards=false
• 关键词: Collaborative; Research; SAI; Integrative; Cyberinfrastructure

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision-making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering.Online abuse is a pressing and growing societal challenge. Online hate and harassment, cyberbullying, and extremism threaten the safety and psychological well-being of targeted groups. Understanding the problem and developing ways to address it is the active focus of many fields of research in the social and behavioral sciences and in computer science. Machine learning and the use of artificial intelligence (AI) offers great potential to support research in this area. Still, researchers face fundamental challenges in leveraging emerging machine learning techniques for innovative studies and scientific discoveries in online abuse. This SAI research project strengthens and transforms the current disperse machine learning software infrastructure. It develops a scalable, customizable, extendable, and user-friendly Integrative Cyberinfrastructure for Online Abuse Research (ICOAR). The new infrastructure advances the research capability for scholars in different fields of science to leverage advanced machine learning methods for online abuse research. The ICOAR software infrastructure can be utilized by a large and growing number of researchers on online abuse detection and is a stimulus to research and innovation in AI for social good.This project enables easy access to state-of-the-art machine learning techniques and datasets for rapid online abuse analysis. It supports and advances future investigations of new concepts and phenomena, assessments of prevalence, measures of causal effects, predictions, and evaluation of online abuse detection algorithms. ICOAR offers a modular and user-centered approach, ensuring future enhancements and long-term sustainability. The open software infrastructure consists of three major layers: a data layer, a capability layer, and an application layer. The data layer includes tools for automatic data collection and preparation of online social media data from different sources, and access to public benchmark datasets. The capability layer is composed of modularized machine learning-based capabilities and algorithms for the study of online abuse. The application layer allows researchers to easily develop different applications based on their research priorities. The ICOAR resources are open-source and provide an easy-to-use learning platform for curriculum development and workforce training.This award is supported by the Directorate for Social, Behavioral, and Economic (SBE) Sciences.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.

加强美国基础设施（SAI）是NSF的一项计划，旨在促进以人为本的基础和潜在的变革性研究，以加强美国的基础设施。有效的基础设施为社会经济活力和广泛改善生活质量奠定了坚实的基础。强大、可靠和有效的基础设施刺激私营部门创新，促进经济增长，创造就业机会，提高公共部门服务提供的效率，加强社区建设，促进机会平等，保护自然环境，增强国家安全，并推动美国的领导地位。为了实现这些目标，需要来自科学和工程学科的专业知识。SAI专注于人类推理和决策，治理以及社会和文化过程的知识如何能够建立和维护有效的基础设施，改善生活和社会，并建立在技术和工程的进步之上。网络仇恨和骚扰、网络欺凌和极端主义威胁着目标群体的安全和心理健康。理解这个问题并开发解决它的方法是社会和行为科学以及计算机科学许多研究领域的积极焦点。 机器学习和人工智能（AI）的使用为支持这一领域的研究提供了巨大的潜力。 尽管如此，研究人员在利用新兴的机器学习技术进行在线滥用的创新研究和科学发现方面面临着根本性的挑战。这个SAI研究项目加强和改造了当前分散的机器学习软件基础设施。它为在线滥用研究（ICOAR）开发了一个可扩展，可定制，可扩展和用户友好的综合网络基础设施。新的基础设施提高了不同科学领域学者的研究能力，以利用先进的机器学习方法进行在线滥用研究。ICOAR软件基础设施可以被越来越多的研究人员用于在线滥用检测，并刺激人工智能的研究和创新，以促进社会公益。该项目可以轻松访问最先进的机器学习技术和数据集，以快速进行在线滥用分析。它支持和推进未来对新概念和现象的调查，对流行率的评估，因果关系的测量，预测和在线滥用检测算法的评估。ICOAR提供模块化和以用户为中心的方法，确保未来的增强和长期的可持续性。 开放式软件基础设施由三个主要层组成：数据层、功能层和应用层。数据层包括用于自动收集数据和准备来自不同来源的在线社交媒体数据以及访问公共基准数据集的工具。能力层由模块化的基于机器学习的能力和算法组成，用于研究在线滥用。应用层允许研究人员根据他们的研究重点轻松开发不同的应用程序。ICOAR资源是开源的，为课程开发和劳动力培训提供了一个易于使用的学习平台。该奖项由社会，行为和经济（SBE）科学理事会支持。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

AAEBERT: Debiasing BERT-based Hate Speech Detection Models via Adversarial Learning

AAEBERT：通过对抗性学习消除基于 BERT 的仇恨言论检测模型的偏差

• 发表时间: 2022
• 期刊: International Conference on Machine Learning and Applications
• 作者: Ebuka Okpala;Long Cheng;N. Mbwambo;Feng Luo
• 通讯作者: Feng Luo

COVID-HateBERT: a Pre-trained Language Model for COVID-19 related Hate Speech Detection

COVID-HateBERT：用于 COVID-19 相关仇恨言论检测的预训练语言模型

• 发表时间: 2021
• 期刊: International Conference on Machine Learning and Applications
• 作者: Mingqi Li;Song Liao;Ebuka Okpala;Max Tong;Matthew Costello;Long Cheng;Hongxin Hu;Feng Luo
• 通讯作者: Feng Luo