Prediction Uncertainty Estimation for Hate Speech Classification

TL;DR

This paper introduces a method for hate speech detection that estimates prediction uncertainty using Monte Carlo dropout, enhancing model reliability and interpretability in classifying harmful content.

Contribution

It adapts deep neural networks with uncertainty estimation for hate speech detection, providing explanations and reliability assessments.

Findings

Effective uncertainty estimation improves detection reliability.

Visualization techniques aid understanding of classification confidence.

Method performs well across different text embeddings.

Abstract

As a result of social network popularity, in recent years, hate speech phenomenon has significantly increased. Due to its harmful effect on minority groups as well as on large communities, there is a pressing need for hate speech detection and filtering. However, automatic approaches shall not jeopardize free speech, so they shall accompany their decisions with explanations and assessment of uncertainty. Thus, there is a need for predictive machine learning models that not only detect hate speech but also help users understand when texts cross the line and become unacceptable. The reliability of predictions is usually not addressed in text classification. We fill this gap by proposing the adaptation of deep neural networks that can efficiently estimate prediction uncertainty. To reliably detect hate speech, we use Monte Carlo dropout regularization, which mimics Bayesian inference within neural networks. We evaluate our approach using different text embedding methods. We visualize the reliability of results with a novel technique that aids in understanding the classification reliability and errors.