An Effective, Robust and Fairness-aware Hate Speech Detection Framework

TL;DR

This paper presents a novel hate speech detection framework that is accurate, robust against attacks, and fair, utilizing data augmentation, uncertainty estimation, and a new neural network architecture, outperforming existing methods.

Contribution

The paper introduces a data-augmented, fairness-aware, and uncertainty-estimated framework with Bidirectional Quaternion-Quasi-LSTM layers for improved hate speech detection.

Findings

Outperforms eight state-of-the-art methods

Effective under attack and no attack scenarios

Generalizes well across multiple datasets

Abstract

With the widespread online social networks, hate speeches are spreading faster and causing more damage than ever before. Existing hate speech detection methods have limitations in several aspects, such as handling data insufficiency, estimating model uncertainty, improving robustness against malicious attacks, and handling unintended bias (i.e., fairness). There is an urgent need for accurate, robust, and fair hate speech classification in online social networks. To bridge the gap, we design a data-augmented, fairness addressed, and uncertainty estimated novel framework. As parts of the framework, we propose Bidirectional Quaternion-Quasi-LSTM layers to balance effectiveness and efficiency. To build a generalized model, we combine five datasets collected from three platforms. Experiment results show that our model outperforms eight state-of-the-art methods under both no attack scenario and various attack scenarios, indicating the effectiveness and robustness of our model. We share our code along with combined dataset for better future research