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Models of Gender Dysphoria Using Social Media Data for Use in Technology-Delivered Interventions: Machine Learning and Natural Language Processing Validation Study

Models of Gender Dysphoria Using Social Media Data for Use in Technology-Delivered Interventions: Machine Learning and Natural Language Processing Validation Study

To identify the unique linguistic content associated with gender dysphoria, 550 n-grams (ie, word units of size n; n=1, 2, and 3) were generated. The first author audited the 250 unigrams, 250 bigrams, and 50 trigrams extracted to ensure that the n-grams were not related to off-topic terms (eg, “lockdown” and “gift card”).

Cory J Cascalheira, Ryan E Flinn, Yuxuan Zhao, Dannie Klooster, Danica Laprade, Shah Muhammad Hamdi, Jillian R Scheer, Alejandra Gonzalez, Emily M Lund, Ivan N Gomez, Koustuv Saha, Munmun De Choudhury

JMIR Form Res 2023;7:e47256