PFI-RP: A Multi-Disciplinary Approach to Detecting Adolescent Online Risks.

PFI-RP：检测青少年在线风险的多学科方法。

• 批准号: 2329976
• 负责人: Pamela Wisniewski
• 金额: $ 75万
• 依托单位: Vanderbilt 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: 2022-10-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2329976&HistoricalAwards=false
• 关键词: PFI; RP; Multi; Disciplinary; Approach

The broader impact/commercial potential of this PFI project is to develop improved social media tools to keep teens safer online. A multidisciplinary team of researchers, clinicians, and industry partners will build, evaluate, and commercialize state-of-the-art technologies that proactively detect adolescent online risk behaviors, including mental health issues, sexual solicitations, and online harassment, for the purpose of mitigating these risks and preventing harm. The team plans to release two separate technology offerings: First, an open source project that shares our risk detection algorithms with developers, so that they can build upon these solutions and form a community dedicated to promoting adolescent online safety. Second, a commercial product that combines these risk detection algorithms into an easy-to-use and accessible service that provides needed support and infrastructure to internet-based companies, who ultimately facilitate the risks teens encounter online, so that they can share in the joint responsibility of protecting the teens who use their platforms. It is imperative that as a society we become more proactive about protect teens from online risks, especially the most vulnerable teens who are at highest risk of engaging in online activities that can lead to physical harm or even death.The proposed project addresses the critical and timely problem of adolescent online safety by leveraging a multi-disciplinary approach of human-centered machine learning to accurately detect risks teen encounter online. The work will be accomplished in four stages: 1) a user-centered phase that examines the types of online risk exposure that matter most to teens and their families. With this insight, the team will build evidence-based adolescent online risk models that identify key dimensions and patterns of risk. 2) a data-driven phase that draws upon these models to establish ground truth and annotate a rich training set of teen social media data, 3) a machine-learning phase that improves existing and builds new risk detection algorithms that are contextualized to teens and able to detect behavioral changes over time, and 4) a technology development phase that evaluates our solution and commercializes it within two separate product lines. The end result will be a suite of algorithms tailored to adolescents that detect objectionable content within social media and identify problematic behavioral patterns or changes that are indicative of impending risks over time. This work will enable real-time online safety interventions that protect and empower teen internet users.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.

这个PFI项目更广泛的影响/商业潜力是开发改进的社交媒体工具，以保持青少年在线安全。一个由研究人员、临床医生和行业合作伙伴组成的多学科团队将建立、评估和商业化最先进的技术，这些技术可以主动检测青少年的在线风险行为，包括心理健康问题、性引诱和在线骚扰，以减轻这些风险并预防伤害。该团队计划发布两个独立的技术产品：首先，一个开源项目，与开发人员分享我们的风险检测算法，以便他们可以在这些解决方案的基础上构建并形成一个致力于促进青少年在线安全的社区。第二，一种商业产品，将这些风险检测算法结合到一个易于使用和访问的服务中，为基于互联网的公司提供所需的支持和基础设施，这些公司最终促进了青少年在网上遇到的风险，以便他们可以分担保护使用其平台的青少年的共同责任。作为一个社会，我们必须更加积极主动地保护青少年免受在线风险的影响，特别是那些最脆弱的青少年，他们从事的在线活动风险最高，可能导致身体伤害甚至死亡。拟议的项目通过利用以人为中心的机器学习的多学科方法来准确检测青少年在线遇到的风险，从而解决青少年在线安全的关键和及时问题。这项工作将分四个阶段完成：1）以用户为中心的阶段，检查对青少年及其家庭最重要的在线风险暴露类型。有了这一见解，该团队将建立基于证据的青少年在线风险模型，以确定风险的关键维度和模式。2)一个数据驱动阶段，利用这些模型来建立基础事实并注释青少年社交媒体数据的丰富训练集; 3）一个机器学习阶段，改进现有的并构建新的风险检测算法，这些算法适用于青少年并能够检测随着时间的推移而发生的行为变化;以及4）一个技术开发阶段，评估我们的解决方案并将其在两个独立的产品线中商业化。最终的结果将是一套针对青少年的算法，可以检测社交媒体中的不良内容，并识别出有问题的行为模式或变化，这些行为模式或变化表明随着时间的推移即将发生的风险。这项工作将使实时在线安全干预，保护和授权青少年互联网用户。这个奖项反映了NSF的法定使命，并已被认为是值得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的支持。