  Racism and intolerance on social media contribute to a toxic online
environment which may spill offline to foster hatred, and eventually lead to
physical violence. That is the case with online antisemitism, the specific
category of hatred considered in this study. Tracking antisemitic themes and
their associated terminology over time in online discussions could help monitor
the sentiments of their participants and their evolution, and possibly offer
avenues for intervention that may prevent the escalation of hatred. Due to the
large volume and constant evolution of online traffic, monitoring conversations
manually is impractical. Instead, we propose an automated method that extracts
antisemitic themes and terminology from extremist social media over time and
captures their evolution. Since supervised learning would be too limited for
such a task, we created an unsupervised online machine learning approach that
uses large language models to assess the contextual similarity of posts. The
method clusters similar posts together, dividing, and creating additional
clusters over time when sub-themes emerge from existing ones or new themes
appear. The antisemitic terminology used within each theme is extracted from
the posts in each cluster. Our experiments show that our methodology
outperforms existing baselines and demonstrates the kind of themes and
sub-themes it discovers within antisemitic discourse along with their
associated terminology. We believe that our approach will be useful for
monitoring the evolution of all kinds of hatred beyond antisemitism on social
platforms.
