RI: Medium: Automatically Understanding and Identifying Digital Expression of Black Grief

在当今世界，多个大规模事件已经汇聚在一起，给美国许多人造成了更大的情绪困扰。除了大规模的事件，如COVID-19，警察对黑人的暴行事件，以及经济衰退，人们也经历了痛苦的个人事件，如失去亲密的家庭成员或朋友。该项目开发了新的基于机器学习的自然语言处理（NLP）工具，以自动识别对这些触发事件做出反应的悲伤和成分情绪的在线表达。重点是黑色的悲伤，这是一种不太了解的现象，特别是当它发生在网络公众。该项目的结果将包括一个数据集，在不同的层次上进行注释，学者和计算研究人员可以使用它来理解黑色悲伤的在线表达，并开发新的NLP模型进行识别。该项目有可能对社会产生真正广泛和深刻的影响。考虑到人们在网上发帖的速度，一个可以自动识别帖子中表达的悲伤的NLP工具对那些回应悲伤的专业人士来说是有用的。自动标记表明发帖者可能需要帮助的帖子将比让专业人员手动扫描所有感兴趣的在线空间更有效，这种方法现在很常见。在该项目期间开发的新NLP工具有可能改变社会工作者，心理健康专业人员和外展工作者在线治疗复杂悲伤的方式，为应对个人数字生活的新干预和治疗计划提供信息。研究人员与黑人哈莱姆居民合作，帮助其他居民科普和处理情绪，包括悲伤和其他令人不安的事件，让他们参与评估开发的NLP工具。这项工作是计算机科学家，社会工作研究人员和语言学家之间的跨学科合作。它包括使用分层注释和计算方法来分析触发（通常是创伤性的）事件后的社交媒体帖子，以确定人们如何就不同类型的损失进行沟通。我们的目标是了解黑人社区成员在帖子中表达悲伤的数字表达。该计划是收集语料库包含悲伤的反应触发事件的表达，并产生一个分层的语料库，反映语义解释和上下文，表达的情绪的心理解释，以及悲伤的语言表达的注释。利用这些数据，将开发一种计算方法来自动识别悲伤，其组成情绪和强度，以及情绪反应如何随时间变化。自然语言处理（NLP）团队将开发新的半监督方法来识别悲伤，其成分情绪和强度，如不同的方言以及会话模式，导致随着时间的推移悲伤的不同解决方案。社会工作团队将对社交媒体帖子注释中嵌入的复杂历史创伤，偏见和种族主义进行定性分析。他们将与社区专家合作，确定最佳策略，用于破译使用超本地语言的不同情感表达，这些语言具有深刻的区域性，细微差别和文化性。语言学团队的工作将促进对特定数字语言策略在创造社会意义中的作用的理解，确定数字语言中形态句法变化的意义。该方法还包括识别在奖项中开发的系统中的种族偏见，并了解当计算模型应用于社区中不同人口统计学特征的语言时对预测的影响（例如，该奖项反映了NSF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。

In today’s world, multiple large-scale events have converged, causing increased emotional distress for many in the United States. In addition to large-scale events, such as COVID-19, incidents of police brutality against Blacks, and the economic downturn, people also experience distressing personal events, such as loss of a close family member or friend. This project develops novel machine learning-based natural language processing (NLP) tools to automatically identify the online expression of grief and component emotions that occur in reaction to these triggering events. The focus is on Black grief, a phenomenon that is not well understood, especially when it occurs in a networked public. The results of this project will include a dataset, annotated at different levels, that scholars and computational researchers can use to understand the online expression of Black grief and develop novel NLP models for its identification. The project has the potential for truly broad and profound impact in society. Given the rate at which people post online, an NLP tool that can automatically identify grief expressed in a post would be useful to professionals who respond to grief. Automatic flagging of posts indicating that the poster may need help would be more efficient than having professionals manually scan all online spaces of interest, an approach that is now common. New NLP tools developed during the project have the potential to shift how social workers, mental health professionals, and outreach workers treat complex grief online, informing new intervention and treatment programs that respond to an individual’s digital life. The investigators work with Black Harlem residents who are helping other residents cope with and process emotions including grief and other disturbing events, engaging them in the evaluation of the developed NLP tools.This work is an interdisciplinary collaboration between computer scientists, social work researchers, and linguists. It includes the use of layered annotation and computational methods to analyze social media posts after triggering, often traumatic, events to identify how people communicate about different types of loss. The goal is to understand the digital expression of grief in posts by Black community members. The plan is to collect corpora containing expressions of grief in reaction to triggering events, and to produce a layered annotation of the corpora reflecting semantic interpretation and context, psychological interpretation of ex- pressed emotion, as well as linguistic expression of grief. Using this data, a computational approach will be developed to automatically identify grief, its component emotions and intensity, and how emotional re- actions change over time. The Natural Language Processing (NLP) team will develop new semi-supervised methods to identify grief, its component emotions and intensity as expressed in different dialects as well as conversational patterns that lead to different resolutions of grief over time. The social work team will perform a qualitative analysis of complex historical trauma, bias, and racism embedded in annotations of social media posts. They will work with community experts to identify the best strategies for deciphering different expressions of emotions that use hyper-local language that is deeply regional, nuanced, and cultural. The linguistics team’s work will advance understanding of the role of specific digital language strategies in the creation of social meaning, identifying the significance of morphosyntactic variation in digital language. The approach also includes identifying racial bias in systems that are developed in the award and understanding the impact on predictions when the computational model is applied to the language of different different demographics in communities (e.g., age, socio-economic status).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.