The investigation of lexical change has predominantly focused on generic language evolution, not suited for detecting shifts in a particular domain, such as hate speech. Our study introduces the task of identifying changes in lexical semantics related to hate speech within historical texts. We present an interdisciplinary approach that brings together NLP and History, yielding a pilot dataset comprising 16th-century Early Modern English religious writings during the Protestant Reformation. We provide annotations for both semantic shifts and hatefulness on this data and, thereby, combine the tasks of Lexical Semantic Change Detection and Hate Speech Detection. Our framework and resulting dataset facilitate the evaluation of our applied methods, advancing the analysis of hate speech evolution.