# Deep Learning for Hate Speech Detection in Tweets

## TL;DR

This paper explores deep learning models for hate speech detection in tweets, demonstrating significant improvements over traditional methods in classifying racist, sexist, or neutral content.

## Contribution

It introduces the application of deep learning architectures with semantic embeddings for hate speech detection, outperforming previous n-gram based approaches.

## Key findings

- Deep learning models outperform n-gram methods by ~18 F1 points
- Semantic word embeddings improve classification accuracy
- Extensive experiments validate the effectiveness of proposed methods

## Abstract

Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis. We define this task as being able to classify a tweet as racist, sexist or neither. The complexity of the natural language constructs makes this task very challenging. We perform extensive experiments with multiple deep learning architectures to learn semantic word embeddings to handle this complexity. Our experiments on a benchmark dataset of 16K annotated tweets show that such deep learning methods outperform state-of-the-art char/word n-gram methods by ~18 F1 points.