CRII: SaTC: Towards Understanding and Defending Against New Waves of Online Hate

CRII：SaTC：理解和防御新一波的网络仇恨

• 批准号: 2245983
• 负责人: Nishant Vishwamitra
• 金额: $ 17.5万
• 依托单位: University of Texas at San Antonio
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2245983&HistoricalAwards=false
• 关键词: CRII; SaTC; Towards; Understanding; Defending

Rapidly changing world events, such as the COVID-19 pandemic, have been accompanied by significant changes in discourse on social media platforms. Online hate has increased and arrives in waves; it is a deeply concerning problem that is negatively transforming the lives of internet users. Recent studies have demonstrated how online hate violates social media policies and describes the ramifications of these violations in the real world. Online hate is difficult to study scientifically. Sparse and biased samples of hate communications are available for computational analysis, especially when hate communications occur and spread suddenly. This project advances the frontiers of knowledge and our ability to defend against abd mitigate online hate. The project applies novel approaches to study waves of online hate, using new computational approaches to detect online hate policy violations and proposing new methods for moderation on social media platforms.To achieve these goals, the investigation is discovering and cataloging novel factors that characterize new waves of online hate. The categorization process is based on temporal and social measurement analyses of online hate communications. The project also is formulating new techniques to effectively discover linkages in social media streams. The key idea is to efficiently sample online hate datasets such that only samples that characterize new instances or forms of online hate are used for machine-learning training. The machine learning paradigm only needs a few samples to effectively learn to detect the new waves of online hate. The project also uses novel techniques to identify and track cross-platform transfers of online hate in user communities across different social media platforms.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.

迅速变化的世界事件，例如COVID-19大流行，伴随着社交媒体平台上的演讲发生重大变化。在线仇恨增加了，并阵阵浪潮。这是一个深切的问题，正在负面改变互联网用户的生活。最近的研究表明，在线仇恨如何违反社交媒体政策，并描述了现实世界中这些侵犯的后果。在线仇恨很难科学学习。稀疏和有偏见的仇恨通信样本可用于计算分析，尤其是在仇恨沟通发生并突然传播时。该项目推动了知识的前沿和我们防御ABD减轻在线仇恨的能力。该项目采用新颖的方法来研究在线仇恨的浪潮，采用新的计算方法来检测在线仇恨政策违规行为，并提出新方法以在社交媒体平台上进行节制。为了实现这些目标，调查正在发现并分类了新的在线仇恨浪潮的新因素。分类过程基于在线仇恨沟通的时间和社会测量分析。该项目还正在制定新技术，以有效地发现社交媒体流中的联系。关键的想法是有效地品尝在线仇恨数据集，以便仅将表征新实例或在线仇恨形式的样本用于机器学习培训。机器学习范式只需要一些样本即可有效学习检测新的在线仇恨浪潮。该项目还使用新颖的技术来识别和跟踪在不同社交媒体平台的用户社区中在线仇恨的跨平台转移。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估标准通过评估来获得支持的。