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DESCRIPTION:Hate speech on social media is a growing concern due to its imp
 act on social cohesion and its potential to incite real-world harm. This s
 tudy 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 colle
 cting additional timeline data\, enabling a comprehensive analysis of user
  behavior before and after posting hate content. Our results reveal signif
 icant differences between hate speech users and control groups\, with high
 er levels of anger\, anxiety\, and negative emotions observed among hate s
 peech users. Pronoun usage patterns suggest these users exhibit greater de
 tachment from others\, with increased use of third-person pronouns and red
 uced use of first-person pronouns. Profanity and moral outrage are initial
 ly high among hate speech users but decrease over time while remaining abo
 ve levels observed in control groups. Furthermore\, topic analysis reveals
  that hate speech topics are more interconnected\, demonstrating higher gl
 obal cohesion and lower topic specificity compared to non-hate content. Th
 ese findings contribute to a deeper understanding of hate speech dynamics 
 on social media and highlight the need for effective interventions to addr
 ess online hate.\n\nCo-sponsored by: Fattane Zarrinkalam\n\nVirtual: https
 ://events.vtools.ieee.org/m/471437
LOCATION:Virtual: https://events.vtools.ieee.org/m/471437
ORGANIZER:fzarrink@uoguelph.ca
SEQUENCE:28
SUMMARY:Exploring Hate Speech Dynamics: The Emotional\, Linguistic\, and Th
 ematic Impact on Social Media Users
URL;VALUE=URI:https://events.vtools.ieee.org/m/471437
X-ALT-DESC:Description: <br /><div data-olk-copy-source="MessageBody">Hate 
 speech on social media is a growing concern due to its impact on social co
 hesion and its potential to incite real-world harm. This study analyzes Tw
 itter data from 6\,002 users to investigate the linguistic and behavioral 
 characteristics of individuals engaging in anti-Asian hate speech during t
 he 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 difference
 s between hate speech users and control groups\, with higher levels of ang
 er\, anxiety\, and negative emotions observed among hate speech users. Pro
 noun usage patterns suggest these users exhibit greater detachment from ot
 hers\, with increased use of third-person pronouns and reduced use of firs
 t-person pronouns. Profanity and moral outrage are initially high among ha
 te speech users but decrease over time while remaining above levels observ
 ed in control groups. Furthermore\, topic analysis reveals that hate speec
 h topics are more interconnected\, demonstrating higher global cohesion an
 d lower topic specificity compared to non-hate content. These findings con
 tribute to a deeper understanding of hate speech dynamics on social media 
 and highlight the need for effective interventions to address online hate.
 </div>
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