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

arXiv: https://arxiv.org/abs/2107.08902 · HTML: https://mlsanity.com/paper/2107.08902/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 23 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes](https://mlsanity.com/paper/2005.04790/) (2020)
- [Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior](https://mlsanity.com/paper/1802.00393/) (2018)
- [Racial Bias in Hate Speech and Abusive Language Detection Datasets](https://mlsanity.com/paper/1905.12516/) (2019)
- [A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media](https://mlsanity.com/paper/1910.12574/) (2019)
- [One-step and Two-step Classification for Abusive Language Detection on Twitter](https://mlsanity.com/paper/1706.01206/) (2017)
- [Automatic Detection of Cyberbullying in Social Media Text](https://mlsanity.com/paper/1801.05617/) (2018)
- [Transfer Learning for Hate Speech Detection in Social Media](https://mlsanity.com/paper/1906.03829/) (2019)
- [Cyberbullying Identification Using Participant-Vocabulary Consistency](https://mlsanity.com/paper/1606.08084/) (2016)

## Built on by (most cited first, 1 of 1)

- [Exploration and Evaluation of Bias in Cyberbullying Detection with Machine Learning](https://mlsanity.com/paper/2412.00609/) (2024)

## Abstract (arXiv)

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.