We study gender-associated stylistic variation in Brazilian Portuguese Google Play reviews. Using IBGE name frequencies, we infer binary gender from first names in 76.7M reviews (96 apps, 2011–2025), obtaining 22.25M high-confidence labels. Women-associated reviews show markedly higher paralinguistic expressivity (about 60% higher emoji density and more lengthening/punctuation), while lexical diversity (MTLD) is nearly identical across groups. Ratings are mostly positive, with men contributing relatively more 1-star reviews and women more 5-star reviews. These findings contribute to a deeper understanding of digital sociolinguistic behavior within the Brazilian context. We discuss limitations of name-based gender inference and future demographic extensions.
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