Research on hate speech detection (HSD) has centered on modern data, even though offensive language has a much longer history. This paper presents the first systematic evaluation of instruction-tuned LLMs on Early Modern English invectives, compared with a modern hate-speech benchmark. Our work applies a modular prompt design to measure the contribution of definitional richness, contextual grounding, decision rules and few-shot examples. The results indicate that clearer annotation boundaries in the curated historical corpus lead to higher classification performance compared to the modern benchmark, despite the disadvantage of linguistic unfamiliarity. Prompt brittleness, however, persists across both domains. Classification-oriented components (rules, examples) drive the strongest effects, while definitional or contextual additions matter less. Fine-tuned encoder models still outperform LLMs, but some prompt configurations can narrow the gap. Overall, our study provides practical guidance for prompt design in both digital humanities and HSD and new opportunities for tracing the historical development of hate speech.

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.