Date of Award:

12-2025

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Committee Chair(s)

Hamid Karimi

Committee

Hamid Karimi

Committee

Steve Petruzza

Committee

Shah Muhammad Hamdi

Abstract

Neurodiversity refers to the natural neurological variations in the human brain such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), dyslexia, dyspraxia, Tourette syndrome and other neurological disorders. it’s estimated that around 15% to 20% of the world’s population is neurodivergent. This means a significant portion of people experience neurological differences in how they think, learn, and interact with the world.

Social Media has become an important space for neurodivergent individuals to share their experiences, build communities, and seek support. This thesis explores how the online venues like X (Twitter) and Reddit offer themselves as digital spaces where neurodivergent voices express the difficulties, abilities, and experiences they possess and go through as neurologically different individual in a neurotypical world.

Using advanced techniques from machine learning and natural language processing, this work analyzed more than 90,000 posts to uncover the key themes and patterns in how neurodivergent people talk about their lives online and how these talks can be useful in creating a more inclusive environment for them in social, professional, and educational set- tings. Text Clustering helped in representing similar types of conversations, while topic modeling methods like LDA and Top2Vec revealed recurring issues such as academic pressure, workplace struggles, emotional resilience, sensory sensitivities, and creative coping strategies. To make these insights more interpretable, the study used GPT-based tools to generate clear summaries and groupings of the discovered topics to reveal global themes.

The findings not only provide a panoramic view of neurodivergent discourse on social media, but also lay the groundwork for designing inclusive educational tools and survey instruments in future research. Ultimately, this work demonstrates how mining online conversations can surface valuable, community-driven knowledge to guide better support systems for neurodivergent individuals in education and beyond.

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