Date of Award

5-12-2024

Degree Type

Dissertation

Degree Name

Doctor of Professional Studies

Department

Information Management

Advisor(s)

Jennifer Stromer-Galley

Second Advisor

Natalie Russo

Keywords

Algorithmic Bias;Autism;Data Justice;Data Science;Depression and Suicide Surveillance;Digital Epidemiology

Abstract

This research study investigates the linguistic characteristics of discourse related to depression and suicidal ideation among autistic women, with the aim of identifying potential biases in digital surveillance models. The inclusion of neurodivergent populations in such models is crucial for ensuring equitable representation and accurate understanding of mental health issues. Data was collected from Reddit, a popular online platform known for its diverse user-generated content. Specifically, two subreddits—r/AutismInWomen and r/aspergirls—dedicated to discussions regarding autism in women were utilized, alongside a general subreddit focused on depression (r/depression). The study employed qualitative methods such as human annotation and topic analysis using BERTopic modeling, as well as quantitative analysis using Linguistic Inquiry Word Count (LIWC) software. Statistical tests were subsequently employed to determine if there were statistically significant differences in variables of discourse between the datasets. The results obtained through this interdisciplinary research shed light on linguistic variations between autistic women's discussions on depression compared to those within general population subreddits dedicated to depression-related conversations. By identifying these differences, potential algorithmic biases can be addressed towards fostering inclusive digital surveillance models that accurately capture neurodivergent experiences related to mental health issues. This study contributes not only to data justice but also holds implications for mental health research, autism advocacy efforts, as well as digital epidemiology practices aimed at monitoring public health trends utilizing online platforms. Ultimately, it underscores the importance of inclusive datasets that encompass diverse perspectives for more equitable representation within algorithm-driven systems.

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Open Access

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