Deep Learning Approaches for Detecting Adversarial Cyberbullying and Hate Speech in Social Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Azumah, Sylvia Worlali, Elsayed, Nelly, ElSayed, Zag, Ozer, Murat, La Guardia, Amanda
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computation and Language Computers and Society
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

_version_	1866916301328351232
author	Azumah, Sylvia Worlali Elsayed, Nelly ElSayed, Zag Ozer, Murat La Guardia, Amanda
author_facet	Azumah, Sylvia Worlali Elsayed, Nelly ElSayed, Zag Ozer, Murat La Guardia, Amanda
contents	Cyberbullying is a significant concern intricately linked to technology that can find resolution through technological means. Despite its prevalence, technology also provides solutions to mitigate cyberbullying. To address growing concerns regarding the adverse impact of cyberbullying on individuals' online experiences, various online platforms and researchers are actively adopting measures to enhance the safety of digital environments. While researchers persist in crafting detection models to counteract or minimize cyberbullying, malicious actors are deploying adversarial techniques to circumvent these detection methods. This paper focuses on detecting cyberbullying in adversarial attack content within social networking site text data, specifically emphasizing hate speech. Utilizing a deep learning-based approach with a correction algorithm, this paper yielded significant results. An LSTM model with a fixed epoch of 100 demonstrated remarkable performance, achieving high accuracy, precision, recall, F1-score, and AUC-ROC scores of 87.57%, 88.73%, 87.57%, 88.15%, and 91% respectively. Additionally, the LSTM model's performance surpassed that of previous studies.
format	Preprint
id	arxiv_https___arxiv_org_abs_2406_17793
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Deep Learning Approaches for Detecting Adversarial Cyberbullying and Hate Speech in Social Networks Azumah, Sylvia Worlali Elsayed, Nelly ElSayed, Zag Ozer, Murat La Guardia, Amanda Machine Learning Computation and Language Computers and Society Cyberbullying is a significant concern intricately linked to technology that can find resolution through technological means. Despite its prevalence, technology also provides solutions to mitigate cyberbullying. To address growing concerns regarding the adverse impact of cyberbullying on individuals' online experiences, various online platforms and researchers are actively adopting measures to enhance the safety of digital environments. While researchers persist in crafting detection models to counteract or minimize cyberbullying, malicious actors are deploying adversarial techniques to circumvent these detection methods. This paper focuses on detecting cyberbullying in adversarial attack content within social networking site text data, specifically emphasizing hate speech. Utilizing a deep learning-based approach with a correction algorithm, this paper yielded significant results. An LSTM model with a fixed epoch of 100 demonstrated remarkable performance, achieving high accuracy, precision, recall, F1-score, and AUC-ROC scores of 87.57%, 88.73%, 87.57%, 88.15%, and 91% respectively. Additionally, the LSTM model's performance surpassed that of previous studies.
title	Deep Learning Approaches for Detecting Adversarial Cyberbullying and Hate Speech in Social Networks
topic	Machine Learning Computation and Language Computers and Society
url	https://arxiv.org/abs/2406.17793