Science Meets Storytelling
Through my writing, I explore how digital spaces shape mental health, trauma, grief, and resilience—particularly for youth and adults of color. I blend rigorous academic research with accessible, human-centered storytelling to elevate underrepresented voices and drive community-centered innovation.
Explore My Work
Academic Publications
Peer-reviewed studies that explore the digital lives of marginalized communities, advancing knowledge in social work, data science, and public health.
Articles and Essays
Essays that reflect on digital grief, emotional well-being, and the intersections of race, identity, and online life.
Media Features
Appearances and commentary in major media outlets unpacking how social platforms affect the emotional and social realities of people of color.
Spotlight on Impact
Digital Narratives of Grief and Resilience: Insights from the Integrating Emotional Stories Online (IESO) Platform
This interdisciplinary study examines how Black Harlem residents and New York City university students use social media to express grief and daily stress. Using the Integrating Emotional Stories Online (IESO) platform, researchers collected anonymous posts and analyzed them with qualitative and computational methods. Findings revealed four main themes: interpersonal grief, systemic grief, everyday stressors, and practices of self-care and joy.
Access My Writing
Contextual Analysis of Social Media: The Promise and Challenge of Eliciting Context in Social Media Posts with Natural Language Processing
This article presents the CASM methodology, a team‑based and community‑informed approach to extracting cultural nuance and context from social media for more precise natural language processing, showing that models trained on hand‑labeled CASM data outperform those using generic distant labeling while addressing ethical concerns about surveillance and harm to marginalized communities .
Detecting and Reducing Bias in a High Stakes Domain
This study reveals that a state‑of‑the art model predicting aggression in social media posts often relies on stop words like “a” or “on” and demonstrates how annotating human rationales and using new robustness metrics can substantially reduce such bias .
Guns on social media: complex interpretations of gun images posted by Chicago youth
This study uses a mixed methods approach to analyze how Black youth in Chicago post images of guns on Twitter and shows that interpretations differ between community experts and social work annotators, highlighting the importance of sociohistorical context in understanding digital behaviors.
When Twitter Fingers Turn to Trigger Fingers: a Qualitative Study of Social Media-Related Gang Violence
This qualitative study draws on interviews with Black males in Chicago with gang involvement and shows how forms of social media communication such as dissing, calling out, and direct threats are interpreted as escalating to real-world violence when not understood through their cultural context.
VATAS: An Open-Source Web Platform for Visual and Textual Analysis of Social Media
This article introduces VATAS, an open‑source web platform for annotating and analyzing both visual and textual content from social media to support more nuanced mixed‑methods research in fields such as social work and digital media analysis.
Detecting Gang-Involved Escalation on Social Media Using Context
This research develops a system grounded in convolutional neural networks that incorporates emotional and semantic context from users’ recent social media posts and interactions to more accurately detect aggression and loss among gang-involved youth.
Humanizing Digital Mental Health through Social Media: Centering Experiences of Gang-Involved Youth Exposed to High Rates of Violence
This perspective reflects on a workshop that used social media posts from gang‑involved youth exposed to community violence to argue for treating digital expressions as vital context in mental health efforts and emphasizes the need for multiple perspectives to interpret them ethically and accurately.
Multimodal social media analysis for gang violence prevention
This study brings together computer scientists and social work researchers to analyze public tweets with images from youth referencing gang associations and shows that combining textual and visual analysis improves the detection of aggression, loss, and substance use which are important psychosocial indicators for early intervention.
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Expressions of loss predict aggressive comments on Twitter among gang-involved youth in Chicago
This study shows that for gang‑involved youth in Chicago posting on Twitter, expressions of loss significantly increase the probability of aggressive comments within the following two days highlighting a crucial intervention window for grief and mental health support.
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