Abstract: Hate speech on social media is a growing concern due to its impact on social cohesion and its potential to incite real-world harm. This study analyzes Twitter data from 6,002 users to investigate the linguistic and behavioral characteristics of individuals engaging in anti-Asian hate speech during the COVID-19 pandemic. We extend a curated dataset by collecting additional timeline data, enabling a comprehensive analysis of user behavior before and after posting hate content. Our results reveal significant differences between hate speech users and control groups, with higher levels of anger, anxiety, and negative emotions observed among hate speech users. Pronoun usage patterns suggest these users exhibit greater detachment from others, with increased use of third-person pronouns and reduced use of first-person pronouns. Profanity and moral outrage are initially high among hate speech users but decrease over time while remaining above levels observed in control groups. Furthermore, topic analysis reveals that hate speech topics are more interconnected, demonstrating higher global cohesion and lower topic specificity compared to non-hate content. These findings contribute to a deeper understanding of hate speech dynamics on social media and highlight the need for effective interventions to address online hate.