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

在当今世界，多个大型事件汇聚在一起，给许多美国人带来了更多的情绪困扰。除了新冠肺炎、警察粗暴对待黑人事件、经济低迷等大型事件外，人们还会经历令人痛心的个人事件，比如失去亲密的家人或朋友。该项目开发了基于机器学习的新型自然语言处理(NLP)工具，以自动识别在线表达的悲伤和组件情绪，这些情绪是对这些触发事件的反应。关注的焦点是黑人悲伤，这一现象并未得到很好的理解，特别是当它发生在联网的公众中时。这个项目的结果将包括一个在不同水平上注释的数据集，学者和计算研究人员可以使用它来理解黑人悲伤的在线表达，并开发新的NLP模型来识别它。该项目有可能在社会上产生真正广泛和深远的影响。考虑到人们在网上发帖的速度，可以自动识别帖子中表达的悲痛的NLP工具对于回应悲痛的专业人士来说将是有用的。自动标记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效率更高，这是一种现在很常见的方法。在该项目期间开发的新的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的干预和治疗计划提供信息。调查人员与黑人哈莱姆居民合作，帮助其他居民应对和处理包括悲伤和其他令人不安的事件在内的情绪，让他们参与到对开发的NLP工具的评估中。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事件(通常是创伤性的)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。目标是了解黑人社区成员在帖子中表达悲伤的数字方式。计划收集包含对触发事件作出反应的悲痛表达的语料库，并对语料库进行分层注释，反映语义解释和上下文、对表达的情感的心理解释以及悲痛的语言表达。利用这些数据，将开发一种计算方法来自动识别悲伤、其组成部分的情绪和强度，以及情绪反应如何随着时间的推移而变化。自然语言处理(NLP)团队将开发新的半监督方法来识别悲伤，它的成分情感和以不同方言表达的强度，以及随着时间的推移导致不同悲伤解决方案的对话模式。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，以破译使用超本地化语言的不同情感表达，这种语言具有深刻的地域性、细致性和文化性。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异的重要性。该方法还包括识别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数据(例如，年龄、社会经济地位)的语言时对预测的影响。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.