While social media platforms are primary vectors for misinformation, automated detection systems remain largely confined to English. This paper presents a transferable, three-stage framework for fine-tuning transformer models to detect domain-specific deceptive content in Spanish. The pipeline comprises: (1) corpus unification, merging fragmented datasets into a 61,674-article resource mapped into three classes (Real, Fake, Satire) to prevent stylistic confounding; (2) systematic model optimization, extensively benchmarking classical metaheuristics against eight transformer architectures (including mBERT, XLM-RoBERTa, and BETO) using strong regularization to mitigate overfitting; and (3) production deployment, encapsulating the optimized model as a containerized web application for real-time inference. Through rigorous experimentation, the Spanish-specific BETO encoder emerged as the strongest model for this task, achieving 89.18% overall accuracy. The model attains a near-perfect in-source F1-score on the satire class; however, a strict source-held-out test reveals that this performance is highly source-dependent—recall on satire from an unseen outlet drops to 0.08—indicating that single-source class construction leads the model to recognize the source rather than a generalizable category. We report this finding as a central methodological result: corpus design, and in particular the source diversity of each class, is the primary determinant of whether the framework generalizes. Adversarial robustness tests using named-entity masking and typo injection provide complementary evidence on the model’s reliance on semantic versus surface cues. The methodology is designed to be adaptable across domains: by substituting the training corpus, the same framework may in principle be retargeted to other digital threats, such as investment scams and phishing, provided that suitable labeled corpora are constructed and validated for each new domain. The complete framework, dataset, and application are released as open-source resources to support reproducible research and practical countermeasures against online misinformation.

Earthquakes are an unpredictable and highly impactful natural phenomenon. In Mexico, the Servicio Sismológico Nacional generates detailed information on earthquakes occurring in the country. Although valuable, this information is not always easy to understand due to its technical level. This work proposes a system that transforms seismic information into visual representations, facilitating analysis. Additionally, the system can relate seismic information with demographic and economic information from the Instituto Nacional de Estadística y Geografía to conduct broader analyses. To guarantee access to the system, open technologies are used and it is freely distributed to all interested users. Los sismos son un fenómeno natural impredecible y con un alto impacto. En México, el Servicio Sismológico Nacional genera información detallada de los sismos ocurridos en el país. Aunque valiosa, esta información no siempre es fácil de comprender debido al nivel técnico. Este trabajo propone un sistema que transforma la información sísmica en representaciones visuales, lo que facilita el análisis; adicionalmente, puede relacionar información sísmica con información demográfica y económica del Instituto Nacional de Estadística y Geografía para realizar análisis más amplios. Para garantizar el acceso a este sistema, se utilizan tecnologías abiertas y distribución libre a todos los usuarios interesados.