This study proposes a regression-based framework for modeling the severity of online hate speech, addressing the limitations of traditional classification approaches that overlook the nuanced and continuous nature of harmful language. Leveraging a compiled dataset of over 3.1 million English-language social media posts from Reddit, Twitter, and Wikipedia, we fine-tune both general-purpose and hate speech-specific transformer models to predict real-valued severity scores. Our findings show that domain-specific models, particularly HateBERT, outperform general-purpose alternatives in capturing subtle gradients of hatefulness. The proposed approach enables more context-aware and proportionate content moderation, while also highlighting challenges related to annotation subjectivity, lack of conversational context, and cross-platform generalization. This work advances the field by demonstrating the feasibility and utility of severity estimation as a scalable alternative to categorical hate speech detection.