Detecting stereotypes is a challenging task, particularly when they are not expressed explicitly. In this study, we applied an annotation schema from the literature designed to formalize implicit stereotypes. We analyzed implicit stereotypes towards immigrants in two datasets: StereoHoax-IT and SterheoSchool, which are created from different sources. StereoHoax-IT consists of reactions on Twitter to specific hoaxes aimed at discriminating against immigrants, while SterheoSchool includes comments from teenagers on fake news generated in psychological experiments. We describe the annotation process, annotator disagreements, and provide both quantitative and qualitative analyses to shed light on how implicitness characterizes stereotypes in different texts. Our findings suggest that implicit stereotypes are often conveyed through logical linguistic relations, such as entailment and behavioral evaluations of immigrants.