Analysing Cyberbullying using Natural Language Processing by Understanding Jargon in Social Media

TL;DR

This paper investigates cyberbullying detection on social media using NLP techniques, combining multiple datasets and models, and introduces a slang-abusive corpus for improved precision.

Contribution

It presents a novel preprocessing approach with a slang-abusive corpus and compares multiple models for enhanced cyberbullying detection accuracy.

Findings

Higher precision achieved with slang preprocessing

Bi-LSTM and BERT outperform other models

Effective detection across diverse cyberbullying types

Abstract

Cyberbullying is of extreme prevalence today. Online-hate comments, toxicity, cyberbullying amongst children and other vulnerable groups are only growing over online classes, and increased access to social platforms, especially post COVID-19. It is paramount to detect and ensure minors' safety across social platforms so that any violence or hate-crime is automatically detected and strict action is taken against it. In our work, we explore binary classification by using a combination of datasets from various social media platforms that cover a wide range of cyberbullying such as sexism, racism, abusive, and hate-speech. We experiment through multiple models such as Bi-LSTM, GloVe, state-of-the-art models like BERT, and apply a unique preprocessing technique by introducing a slang-abusive corpus, achieving a higher precision in comparison to models without slang preprocessing.