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

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.