CAREER: Socially-Aware Language Technologies To Support People in Supporting Others for Better Online Communities

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。这项研究将为在线同伴支持小组发明一套新的社会意识语言技术，包括在嘈杂和资源匮乏的环境中自动预测文本帮助技能的机器学习分类器，以及有效地为支持者生成量身定制和情境化帮助的语言生成技术。提供支持的人是这些在线小组取得成功的关键，数百万有健康问题的人使用这些小组。然而，网络支持者往往没有接受严格的培训和量身定制的反馈，这可能会导致不支持甚至消极的帮助行为。现有的培训或辅助机制在很大程度上依赖于人力监督，因此很难扩大规模，以帮助为数众多的支持者，这些支持者为数百万需要照顾的人提供支持。这项工作有可能以最少的人力推进最先进的技术并扩展到许多不同的其他领域。通过开发、部署和评估赋予社会重要领域支持者权力的新干预措施，这项工作拓宽了对技术用于心理健康同伴支持的科学理解。通过将计算机科学与在线同伴支持小组的研究相结合，这项工作将吸引那些可能不会被科学和工程职业所吸引的学生，包括女性和代表性不足的群体的成员。本研究将通过以下方式实现在几个具有代表性的基于文本的在线同伴支持小组中支持人们更好地支持他人的愿景：(1)开发创新的自然语言处理技术来预测支持者的帮助技能，并研究帮助技能与积极结果的关系；(2)设计情境化语言生成方法，通过强调在特定情况下需要哪些帮助技能，并提出具有可操作反馈的示例回应，为支持者提供量身定制的帮助；(3)创建一个开源和人在循环的工具，以授权支持者和评估如何工具可以用于培训和实时脚手架通过实验室研究，现场实验，和现实世界的部署。结果将是社会科学理论和自然语言处理（NLP）社区有益进展的新颖综合，开拓这一新兴研究领域，使用NLP来支持心理健康和福祉。具体地说，它将发展关于支持者如何使用不同的帮助技能来帮助寻求者的科学知识，并深入了解这种支持交换如何与积极结果相关。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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). This research will invent a set of new socially aware language technologies designed for online peer support groups, including machine learning classifiers that automatically predict helping skills from text in noisy and low-resourced settings, and language generation techniques that effectively generate tailored and contextualized assistance for supporters. The people who provide support are the key to the success of these online groups, which are used by millions of people with health concerns. However, online supporters often do not receive rigorous training and tailored feedback, which might lead to unsupportive or even negative helping behaviors. Existing mechanisms of training or scaffolding largely rely on human supervision, making it hard to scale up to help the large number of supporters who support millions of people in need of care. This work has the potential to advance the state-of-the-art and scale up to many different other domains with minimal human effort. By developing, deploying, and evaluating new interventions that empower supporters in socially important domains, this work broadens the scientific understanding of technology use for mental health peer support. By combining computer science with the study of online peer support groups, this work will appeal to students who might not otherwise be attracted to science and engineering careers, including women and members of underrepresented groups.This work will accomplish the vision of supporting people in better supporting others in several representative text-based online peer support groups by: (1) developing innovative natural language processing techniques to predict supporters' helping skills and examining how helping skills related to positive outcomes; (2) designing contextualized language generation approaches that provide tailored assistance for supporters by highlighting which helping skills are needed in a given situation and suggesting example responses with actionable feedback; and (3) creating an open-source and human-in-the-loop tool to empower supporters and evaluating how the tool can be used for both training and real-time scaffolding via lab studies, field experiments, and real-world deployment. The result will be a novel synthesis of social science theories and beneficial advances in natural language processing (NLP) communities, to pioneer this emerging research field that uses NLP to support mental health and well-being. Concretely, it will develop scientific knowledge of how supporters use different helping skills to help seekers, and a deep understanding of how such support exchange relates to positive outcomes.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.