Hate speech and hate crimes: a data-driven study of evolving discourse around marginalized groups

Malvina Bozhidarova; Jonathn Chang; Aaishah Ale-rasool; Yuxiang Liu; Chongyao Ma; Andrea L. Bertozzi; P. Jeffrey Brantingham; Junyuan Lin; Sanjukta Krishnagopal

Hate speech and hate crimes: a data-driven study of evolving discourse around marginalized groups

Malvina Bozhidarova, Jonathn Chang, Aaishah Ale-rasool, Yuxiang Liu, Chongyao Ma, Andrea L. Bertozzi, P. Jeffrey Brantingham, Junyuan Lin, Sanjukta Krishnagopal

Mathematics, Statistics and Data Science

Research output: Other contribution

Abstract

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.

Original language	English
Publisher	arXiv
State	Published - Nov 1 2023

Keywords

COMPLETED
Email
DEPARTMENT: Mathematics
Computer Science - Social and Information Networks
Email [email protected]
Statistics - Applications
Statistics - Computation

Access to Document

http://arxiv.org/abs/2311.11163

Cite this

@misc{6422cb8dc92943b2bc8fef33805a3e90,

title = "Hate speech and hate crimes: a data-driven study of evolving discourse around marginalized groups",

abstract = "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.",

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author = "Malvina Bozhidarova and Jonathn Chang and Aaishah Ale-rasool and Yuxiang Liu and Chongyao Ma and Bertozzi, {Andrea L.} and Brantingham, {P. Jeffrey} and Junyuan Lin and Sanjukta Krishnagopal",

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T1 - Hate speech and hate crimes: a data-driven study of evolving discourse around marginalized groups

AU - Bozhidarova, Malvina

AU - Chang, Jonathn

AU - Ale-rasool, Aaishah

AU - Liu, Yuxiang

AU - Ma, Chongyao

AU - Bertozzi, Andrea L.

AU - Brantingham, P. Jeffrey

AU - Lin, Junyuan

AU - Krishnagopal, Sanjukta

N1 - arXiv:2311.11163 [cs, stat]

PY - 2023/11/1

Y1 - 2023/11/1

N2 - 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.

AB - 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.

KW - COMPLETED

KW - Email

KW - DEPARTMENT: Mathematics

KW - Computer Science - Social and Information Networks

KW - Email [email protected]

KW - Statistics - Applications

KW - Statistics - Computation

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