Authors :
RavinathaHiththatiya; Yasiru Silva; Dushan Fernando; Dr. Lakmal Rupasinghe; Shehan Kodagoda
Volume/Issue :
Volume 8 - 2023, Issue 6 - June
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/3e677ayp
DOI :
https://doi.org/10.5281/zenodo.8124230
Abstract :
While online communication can be a great
tool for sharing knowledge and opinions, it can also lead
to cyberbullying and hatred towards individuals, and
the popular Discord platform is no exception. This
research aims to create a bot that can prevent
cyberbullying incidents in the Discord app. Also, by
focusing on the overall security of the bot, the proposed
system aims to detect and prevent anomalies and SQL
injection attacks. The introduction provides an
overview of cyberbullying on Discord and other social
media platforms. In the next section, it gives a detailed
understanding of past research conducted in the realm
of cyberbullying detection on social media using natural
language processing techniques and deep learning. The
Methodology section focuses on the system architecture
and design of text classification models, image
classification models, audio classification models, and
bot security. The proposed system effectively uses
advanced natural language processing techniques and
various machine learning classifiers to accurately detect
cyberbullying messages in the domains of text, image,
and audio on the Discord app.
Keywords :
Cyberbullying Detection, Machine Learning Models, Phishing Link Detection, SQL Injection Detection, Content Analysis, Speech-to-Text Conversion, Optical Character Recognition, Event-Driven Programming, Discord Server.
While online communication can be a great
tool for sharing knowledge and opinions, it can also lead
to cyberbullying and hatred towards individuals, and
the popular Discord platform is no exception. This
research aims to create a bot that can prevent
cyberbullying incidents in the Discord app. Also, by
focusing on the overall security of the bot, the proposed
system aims to detect and prevent anomalies and SQL
injection attacks. The introduction provides an
overview of cyberbullying on Discord and other social
media platforms. In the next section, it gives a detailed
understanding of past research conducted in the realm
of cyberbullying detection on social media using natural
language processing techniques and deep learning. The
Methodology section focuses on the system architecture
and design of text classification models, image
classification models, audio classification models, and
bot security. The proposed system effectively uses
advanced natural language processing techniques and
various machine learning classifiers to accurately detect
cyberbullying messages in the domains of text, image,
and audio on the Discord app.
Keywords :
Cyberbullying Detection, Machine Learning Models, Phishing Link Detection, SQL Injection Detection, Content Analysis, Speech-to-Text Conversion, Optical Character Recognition, Event-Driven Programming, Discord Server.
