Date of Award:

5-2026

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Committee Chair(s)

Shah Muhammad Hamdi

Committee

Shah Muhammad Hamdi

Committee

Soukaina Filali Boubrahimi

Committee

Steve Petruzza

Abstract

Social media platforms are a central part of modern communication, shaping how people share ideas, build communities, and discuss social issues. While these spaces can support connection and self expression, they also enable the spread of harmful language such as hate speech. At the same time, social media is an important place where members of marginalized communities, including sexual and gender minorities, express stress, discrimination, and emotional challenges in ways that are often indirect and context dependent.

This research examines whether modern artificial intelligence systems can better identify harmful language and expressions of minority stress in online posts. The study evaluates advanced language models using large collections of real world social media data to under-stand how well these systems interpret meaning, context, and social relationships.

The results show that these models can effectively detect harmful discourse, especially when they are trained with high quality data and social context. However, they still struggle with subtle language such as sarcasm and implied hostility. Overall, this work highlights the potential of artificial intelligence to help create safer online environments and to support research on mental health and social well being in digital spaces.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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