The article presents a case study on Antisemitic hate speech in Twitter in the period September 2019-May 2020, with a particular focus on the months of the Covid-19 emergency. The corpus, consisting of 160.646 tweets selected by keywords, was investigated in terms of the amount of hate for each month, rhetoric and forms of Antisemitism. The analysis is carried out through social network analysis (SNA) techniques, with the goalof understanding whether it is possible to automate the process of identifying Antisemitic hatred. 26.11% of tweets contain hatred, that prejudice is the most commonrhetoric (44%) and association with financial power the prevailing form (74%). The sample was also compared with another research methodology that only detects the presence of hate words. It emerges that, in addition to an in-depth knowledge of the phenomenon, it is necessary to integrate the automatic classification phase with the manual contribution.