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

这个PFI项目的更广泛的影响/商业潜力是开发改进的社交媒体工具，使青少年更安全地上网。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，以主动检测青少年在线风险行为，包括心理健康问题、性引诱和在线骚扰，以减轻这些风险并防止伤害。该团队计划发布两项独立的技术产品：首先是一个开源项目，与开发人员共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成一种易于使用和访问的服务，为基于互联网的公司提供所需的支持和基础设施，最终促进青少年在网上遇到的风险，这样他们就可以共同承担保护使用其平台的青少年的责任。作为一个社会，我们必须更加积极主动地保护青少年免受网络风险的侵害，尤其是那些最脆弱的青少年，他们参与网络活动的风险最高，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网上遇到的风险，解决了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，该团队将建立以证据为基础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真相，并注释丰富的青少年社交媒体数据训练集；3)一个机器学习阶段，改进现有的和建立新的风险检测算法，这些算法与青少年的背景相结合，能够检测到随着时间的推移的行为变化；4)一个技术开发阶段，评估我们的解决方案，并在两个独立的产品线中将其商业化。最终的结果将是一套为青少年量身定制的算法，用于检测社交媒体中的不良内容，并识别有问题的行为模式或随着时间的推移预示着即将到来的风险的变化。这项工作将使实时在线安全干预成为可能，从而保护青少年互联网用户并赋予他们权力。该奖项反映了美国国家科学基金会的法定使命，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。

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.