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Revisiting the Semantic Severity of Anxiety and Depression: Computational Linguistic Study of Normalization and Pathologization

Revisiting the Semantic Severity of Anxiety and Depression: Computational Linguistic Study of Normalization and Pathologization

First, we trained a Continuous Bag of Words word2vec model on the entire corpus using the word2vec package in R (R Foundation for Statistical Computing) [27]. Next, we created a mental health vector by averaging the vectors of words strongly associated with mental health (eg, therapy, psychiatry, and diagnosis; see Multimedia Appendix 1 for details). This vector was used as a reference point for identifying mental health–related language.

Vojtech Pisl, Ana-Maria Bucur, Ioana R Podina

J Med Internet Res 2025;27:e73950

Chicago Public Health Department Social Media Communications on Twitter During the COVID-19 Pandemic and the Mpox Epidemic: Cross-Sectional Content Analysis

Chicago Public Health Department Social Media Communications on Twitter During the COVID-19 Pandemic and the Mpox Epidemic: Cross-Sectional Content Analysis

Indeed, as Moore et al [16] have written, “when Jonathan Swift wrote, ‘falsehood flies, and the truth comes limping after it,’ in 1710 he could easily have been describing the state of social media use for public health in [the present day].” Results from this study demonstrate the CDPH used X to exclusively engage in 1-way communication during the study period and did not engage in 2-way dialogue or to reply to misinformation and disinformation.

Matthew R Boyce, Margot Gordon, Rachael Piltch-Loeb, Rebecca Katz

J Med Internet Res 2025;27:e68200