A Hybrid Deep Learning Approch for Cyberbullying Detection in Twitter On social Media Platform
Abstract
Cyberbullying on social media has become a
significant concern due to the widespread use of online
platforms. Unlike traditional bullying, cyberbullying
can occur anonymously, instantly, and on a massive
scale, making it harder to detect and control. This paper
proposes an AI-based detection system that identifies
offensive, threatening, or harmful comments on social
media. Using natural language processing (NLP)
techniques along with machine learning and deep
learning models, the system is trained to distinguish
between normal and abusive language patterns. The
model is evaluated using labeled datasets,
demonstrating high accuracy in detecting cyberbullying
across various social media texts. This approach aims
to assist platform moderators and create safer digital
environments.
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