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Cascalheira CJ, Flinn RE, Zhao Y, Klooster D, Laparade D, Hamdi SM, Scheer JR, Gonzalez A, Lund EM, Gomez IN, Saha K, De Choudhury M
Models of Gender Dysphoria Using Social Media Data for Use in Technology-Delivered Interventions: Machine Learning and Natural Language Processing Validation Study
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Modeling Gender Dysphoria with Machine Learning and Natural Language Processing: Preliminary Implications for Technology-Delivered Interventions
Cory J. Cascalheira;
Ryan E. Flinn;
Yuxuan Zhao;
Dannie Klooster;
Danica Laparade;
Shah M. Hamdi;
Jillian R. Scheer;
Alejandra Gonzalez;
Emily M. Lund;
Ivan N. Gomez;
Koustuv Saha;
Munmun De Choudhury
ABSTRACT
Background:
Many transgender and nonbinary (TNB) people face significant treatment barriers (e.g., healthcare discrimination) when seeking help for gender dysphoria. Technology-delivered interventions for TNB people can be used discretely, safely, and flexibly, thereby reducing such treatment barriers. Technology-delivered interventions are beginning to incorporate machine learning (ML) and natural language processing (NLP) to automate intervention components and tailor intervention content. A critical step in using ML and NLP in technology-delivered interventions is demonstrating how accurately these methods model gender dysphoria.
Objective:
The present study sought to determine the preliminary effectiveness of modeling gender dysphoria with ML and NLP.
Methods:
Six ML models and 949 NLP-generated independent variables were used to model gender dysphoria from the text data of 1,573 Reddit posts created on TNB-specific online forums. Qualitative content analysis was used to determine whether gender dysphoria was present in each post (i.e., the dependent variable). NLP transformed the linguistic content of each post into predictors for the ML algorithms.
Results:
Results indicated that a supervised ML algorithm (i.e., optimized extreme gradient boosting; XGBoost) modeled gender dysphoria with a high degree of accuracy (.84), precision (.83), and speed (1.23 seconds). Of the NLP-generated independent variables, DSM-5 clinical keywords (e.g., dysphoria, disorder) were most predictive of gender dysphoria.
Conclusions:
These preliminary findings and initial validation evidence suggest ML- and NLP-based models of gender dysphoria have significant potential to be integrated into TNB-specific technology-delivered interventions.
Citation
Please cite as:
Cascalheira CJ, Flinn RE, Zhao Y, Klooster D, Laparade D, Hamdi SM, Scheer JR, Gonzalez A, Lund EM, Gomez IN, Saha K, De Choudhury M
Models of Gender Dysphoria Using Social Media Data for Use in Technology-Delivered Interventions: Machine Learning and Natural Language Processing Validation Study