CAREER: Machine Learning-Based Approaches Toward Combatting Abusive Behavior in Online Communities

职业：基于机器学习的方法来打击在线社区中的虐待行为

• 批准号: 1553376
• 负责人: Eric Gilbert
• 金额: $ 54.89万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-02-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1553376&HistoricalAwards=false
• 关键词: CAREER; Machine; Learning; Based; Approaches

This research aims to computationally model abusive online behavior to build tools that help counter it, with the goal of making the Internet a more welcoming place. Since its earliest days, flaming, trolling, harassment and abuse have plagued the Internet. This project will lay bare the structure of online abuse over many types of online conversations, a major step forward for the study of computer-mediated communication. This will result from modeling abuse with statistical machine learning algorithms as a function of theoretically inspired, sociolinguistic variables, and will entail new technical and methodological advances. This work will enable a transformative new class of automated and semi-automated applications that depend on computationally generated abuse predictions. The education and outreach plan is deeply tied to the research activities, and focuses on scaling-up the research's broader impacts. A public application programming interface (API) will enable developers and online community managers around the world to integrate into their own sites the defenses against abuse developed by this research.The work will consist of two major phases. In the first, the research will develop a deep understanding of abusive online behavior via statistical machine learning techniques. Specifically, the work will appropriate theories from social science and linguistics to inform the creation of features for robust statistical machine learning algorithms to predict abuse. These proposed abuse models will enable a brand new, transformative class of mixed-initiative artifacts capable of intervening in social media and online communities. In the second phase, this project will explore this newly enabled class of artifacts by building, deploying and evaluating sociotechnical tools for combatting abuse. Specifically, it will explore two classes of tools that use the abuse predictions: shields and moderator tools. The first, shields, will proactively block inbound abuse from reaching people. The second class of tools, moderator tools, will flag and triage abuse for community moderators.

这项研究的目的是通过计算建模滥用在线行为，以建立有助于对抗它的工具，目的是使互联网成为一个更受欢迎的地方。 从最早的时候起，燃烧，拖钓，骚扰和虐待就一直困扰着互联网。该项目将揭示多种类型的在线对话中的在线滥用结构，这是计算机介导通信研究的重要一步。这将是由于使用统计机器学习算法作为理论启发的社会语言学变量的函数来建模滥用，并将带来新的技术和方法上的进步。这项工作将使一个变革性的新一类自动化和半自动化的应用程序，依赖于计算生成的滥用预测。教育和推广计划与研究活动密切相关，重点是扩大研究的广泛影响。 一个公共应用程序编程接口（API）将使世界各地的开发人员和在线社区管理人员能够将本研究开发的防滥用措施集成到自己的网站中。首先，该研究将通过统计机器学习技术深入了解滥用在线行为。具体来说，这项工作将采用社会科学和语言学的理论，为强大的统计机器学习算法创建功能，以预测滥用行为。这些拟议的滥用模型将使一个全新的，变革性的混合主动工件类能够干预社交媒体和在线社区。在第二阶段，该项目将通过构建、部署和评估用于打击虐待的社会技术工具来探索这类新启用的工件。具体来说，它将探讨两类使用滥用预测的工具：盾牌和仲裁员工具。第一个是盾牌，它将主动阻止传入的滥用到达人们。第二类工具，版主工具，将为社区版主标记和分类滥用。