With the increasing popularity of social media platforms, the dissemination of misogynistic content has become more prevalent and challenging to address. In this paper, we investigate the phenomenon of online misogyny on Twitter through the lens of hurtfulness, qualifying its different manifestation considering the profession of the targets of misogynistic attacks.By leveraging manual annotation and a BERTweet model trained for fine-grained misogyny identification, we find that specific types of misogynistic speech are more intensely directed towards particular professions: derailing discourse predominantly targets authors and cultural figures, while dominance-oriented speech and sexual harassment are mainly directed at politicians and athletes. Additionally, we use the HurtLex lexicon and ItEM to assign hurtfulness scores to tweets based on different hate speech categories. Our analysis reveals that these scores align with the profession-based distribution of misogynistic speech, highlighting the targeted nature of such attacks.