Foodborne illnesses present an ongoing risk to public health, affecting millions of individuals each year. As such, there is a need to control food safety. Recent developments in machine and deep learning technologies have facilitated the prediction of food risks. Existing approaches often rely on data sources beyond text, including images and structured data. Among those that use textual sources, these are often elaborated and already edited texts and not near real-time, as social data can be. However, these works still make use of outdated methodologies like classical machine learning models (such as Support Vector Machine (SVM) or Bayesian networks). Although Language Models have a significant ability to work with textual data, they are little explored.