A Computational Framework for Measuring and Analyzing Gender Bias in Portuguese-language Literary Texts - Silva, Mariana O.; Brandão, Michele A.; Moro, Mirella M. (Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais Estendidos do XLI Simpósio Brasileiro de Bancos de DadosDO - 10.5753/sbbd_estendido.2026.249414, 2026)
Literary texts often reproduce and reshape social biases, including gender bias, through recurring linguistic patterns that influence how characters are represented. While traditional literary criticism offers detailed interpretations, it is limited in scale, and most computational studies of gender bias focus on English, leaving Portuguese-language literature underexplored. This dissertation addresses this gap by proposing PORTALIA, a modular computational framework for measuring and analyzing gender bias in Portuguese-language literary texts. The framework integrates Natural Language Processing techniques for character identification, automated gender inference, linguistic analysis, and quantitative bias measurement. It is evaluated on Portuguese literary corpora and supports large-scale, reproducible analyses of gendered patterns in character representation, descriptors, and narrative agency.