This study analyzes texts from multiple sources, including social media and news portals, to observe how different sectors of Brazilian society discuss the antimicrobial resistance. The main goal is to support epidemiological surveillance and public policy decisions through computational tools. Three datasets were used: tweets collected between 2008 and 2025 (64,225 documents), news articles from G1 (4,363 documents), and official government publications (.gov.br, 1,515 documents). These sources enable comparative analysis between informal discourse (social media) and institutional or journalistic discourse (official and media outlets). The study applies and compares topic modeling techniques, particularly those designed for Short Text Topic Modeling (STTM), such as GSDMM and BERTopic, to identify discursive trends, semantic patterns, and emerging topics related to antimicrobial resistance. By exploring these distinct contexts, this work demonstrates the potential of Natural Language Processing (NLP) and AI methods as instruments for integrated analysis of public health data in both informal and formal environments.