English offensive text detection using CNN based Bi-GRU model

TL;DR

This paper introduces a Bi-GRU-CNN model for detecting offensive text on social media, improving accuracy over existing methods by combining deep learning techniques.

Contribution

The paper presents a novel Bi-GRU-CNN architecture specifically designed for offensive text detection, demonstrating superior performance compared to prior models.

Findings

The proposed model outperforms existing models in offensive text classification.

Bi-GRU-CNN achieves higher accuracy and precision.

The combined deep learning approach effectively identifies offensive content.

Abstract

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.