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) 是一项 NSF 计划，旨在促进以人为本的基础性和潜在变革性研究，加强美国的基础设施，为社会经济活力和广泛的生活质量改善提供坚实的基础。私营部门创新、发展经济、创造就业机会、提供更多公共部门服务、加强社区、促进平等机会、保护自然环境、增强国家安全并增强美国的领导力。要实现这些目标，需要来自各个领域的专业知识。 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构建和维护有效的基础设施，从而改善生活和社会，并以技术和工程的进步为基础。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，是一个紧迫且日益严重的社会挑战。以及行为科学和计算机科学。尽管如此，机器学习和人工智能 (AI) 的使用为支持该领域的研究提供了巨大的潜力，但研究人员在利用新兴机器学习技术进行在线滥用方面的创新研究和科学发现方面面临着根本性的挑战。它开发了一个可扩展、可定制、可扩展且用户友好的在线滥用研究综合网络基础设施（ICOAR），新的基础设施提高了不同科学领域的学者利用先进机器学习的研究能力。方法ICOAR 软件基础设施可供大量且不断增长的在线滥用检测研究人员使用，并刺激人工智能领域的研究和创新，造福社会。该项目可以轻松获取最新技术。用于快速在线滥用分析的艺术机器学习技术和数据集。它支持和推进对新概念和现象的未来调查、流行率评估、因果效应测量、预测和在线滥用检测算法的评估。以中心为中心的方法，确保未来的增强和长期开放软件基础设施由三个主要层组成：数据层、能力层和应用层。数据层包括用于自动收集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的工具。能力层由基于模块化机器学习的功能和算法组成，用于研究在线滥用行为。应用程序层允许研究人员根据其研究重点轻松开发不同的应用程序。 -使用学习平台进行课程开发和劳动力培训。这该奖项由社会、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

AAEBERT: Debiasing BERT-based Hate SpeechDetection Models via Adversarial Learning

AAEBERT：通过对抗性学习消除基于 BERT 的仇恨语音检测模型的偏差

• 发表时间: 2022-01
• 期刊: International Conference on Machine Learning and Applications
• 作者: Okpala, Ebuka;Cheng, Long;Mbwambo, Nicodemus;Luo, Feng
• 通讯作者: Luo, Feng

COVID-HateBERT: a Pre-trained Language Modelfor 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: 2021-01
• 期刊: International Conference on Machine Learning and Applications
• 作者: Mingqi, Li;Liao, Song;Okpala, Ebuka;Tong, Ma;Costello, Matthew;Cheng, Long;Hu, Hongxin;Luo, Feng
• 通讯作者: Luo, Feng