# English offensive text detection using CNN based Bi-GRU model

arXiv: https://arxiv.org/abs/2409.15652 · HTML: https://mlsanity.com/paper/2409.15652/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 24 Sep 2024.

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

- [Deep Learning Models for Multilingual Hate Speech Detection](https://mlsanity.com/paper/2004.06465/) (2020)
- [Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition](https://mlsanity.com/paper/2002.10361/) (2020)
- [Detecting Hate Speech and Offensive Language on Twitter using Machine Learning: An N-gram and TFIDF based Approach](https://mlsanity.com/paper/1809.08651/) (2018)
- [Cross-lingual Capsule Network for Hate Speech Detection in Social Media](https://mlsanity.com/paper/2108.03089/) (2021)
- [Highly Generalizable Models for Multilingual Hate Speech Detection](https://mlsanity.com/paper/2201.11294/) (2022)
- [PhishGuard: A Convolutional Neural Network Based Model for Detecting Phishing URLs with Explainability Analysis](https://mlsanity.com/paper/2404.17960/) (2024)
- [Enhancing Bangla Language Next Word Prediction and Sentence Completion through Extended RNN with Bi-LSTM Model On N-gram Language](https://mlsanity.com/paper/2405.01873/) (2024)
- [Sentiment Polarity Analysis of Bangla Food Reviews Using Machine and Deep Learning Algorithms](https://mlsanity.com/paper/2405.06667/) (2024)

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

- [Abstractive Text Summarization for Bangla Language Using NLP and Machine Learning Approaches](https://mlsanity.com/paper/2501.15051/) (2025)

## Abstract (arXiv)

Over the years, the number of users of social media has increased drastically. People frequently share their thoughts through social platforms, and this leads to an increase in hate content. In this virtual community, individuals share their views, express their feelings, and post photos, videos, blogs, and more. Social networking sites like Facebook and Twitter provide platforms to share vast amounts of content with a single click. However, these platforms do not impose restrictions on the uploaded content, which may include abusive language and explicit images unsuitable for social media. To resolve this issue, a new idea must be implemented to divide the inappropriate content. Numerous studies have been done to automate the process. In this paper, we propose a new Bi-GRU-CNN model to classify whether the text is offensive or not. The combination of the Bi-GRU and CNN models outperforms the existing model.