Document Type

Poster

Publication Date

Fall 2024

Abstract

Do autism diagnostic self-narratives have something in common? The meaning of diagnostic labels is a culturally relevant topic in 2024, and I am curious to know if there are similarities in self-narratives about autism diagnosis. We are at an interesting moment in regards to our attitudes about autism and disability, where diagnostic labels like autism are usually viewed as either a tragedy or a relief. I wanted to look at three recent autism diagnostic self-narratives from late-diagnosed women to confirm or refute the existence of a specific type of “narrative” around diagnosis. I was additionally interested in how artificial intelligence programs might understand words and topics related to disabilities. This project uses sentiment analysis to evaluate and compare the shapes of three different late-diagnostic narratives by autistic women from news websites and blogs. Additionally, this project explores an important methodological question in computational humanities: what is the ideal window size for running sentiment analysis programs on shorter texts like the narratives that I chose? Previously not knowing the proper window size for a text so short, I ended up experimenting with many different sizes to see which one would allow the model to assess the peaks and valleys of each narrative most accurately. Window size refers to the amount of data that each crux point is extracted from. This research has implications for AI ethics and anti-bias training, as well as for digital humanities research on new types of narratives.

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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