Investigating Deep Learning Approaches for Hate Speech Detection in Social Media

TL;DR

This paper explores deep learning methods with different embeddings to improve hate speech detection on social media, addressing challenges like data variability and limited context.

Contribution

It introduces deep learning approaches tailored for hate speech detection, demonstrating significant accuracy improvements across multiple datasets.

Findings

Significant accuracy and F1-score improvements achieved.

Effective handling of diverse hate speech types.

Robust detection despite limited contextual information.

Abstract

The phenomenal growth on the internet has helped in empowering individual's expressions, but the misuse of freedom of expression has also led to the increase of various cyber crimes and anti-social activities. Hate speech is one such issue that needs to be addressed very seriously as otherwise, this could pose threats to the integrity of the social fabrics. In this paper, we proposed deep learning approaches utilizing various embeddings for detecting various types of hate speeches in social media. Detecting hate speech from a large volume of text, especially tweets which contains limited contextual information also poses several practical challenges. Moreover, the varieties in user-generated data and the presence of various forms of hate speech makes it very challenging to identify the degree and intention of the message. Our experiments on three publicly available datasets of different domains shows a significant improvement in accuracy and F1-score.