  This study explores the dynamic relationship between online discourse, as
observed in tweets, and physical hate crimes, focusing on marginalized groups.
Leveraging natural language processing techniques, including keyword extraction
and topic modeling, we analyze the evolution of online discourse after events
affecting these groups. Examining sentiment and polarizing tweets, we establish
correlations with hate crimes in Black and LGBTQ+ communities. Using a
knowledge graph, we connect tweets, users, topics, and hate crimes, enabling
network analyses. Our findings reveal divergent patterns in the evolution of
user communities for Black and LGBTQ+ groups, with notable differences in
sentiment among influential users. This analysis sheds light on distinctive
online discourse patterns and emphasizes the need to monitor hate speech to
prevent hate crimes, especially following significant events impacting
marginalized communities.
