DeL-haTE: A Deep Learning Tunable Ensemble for Hate Speech Detection

TL;DR

DeL-haTE introduces a deep learning ensemble with transfer learning and weak supervision to improve hate speech detection across mainstream and fringe social media platforms, addressing data scarcity and evolving language.

Contribution

It presents a novel ensemble framework with transfer learning and weak supervision for hate speech detection, especially effective on unlabeled fringe platform data.

Findings

Achieved 83% hate recall on HON dataset, outperforming existing models.

Enhanced hate detection on Gab with 67% recall using weak supervision.

Demonstrated the effectiveness of ensemble and transfer learning in diverse social media contexts.

Abstract

Online hate speech on social media has become a fast-growing problem in recent times. Nefarious groups have developed large content delivery networks across several main-stream (Twitter and Facebook) and fringe (Gab, 4chan, 8chan, etc.) outlets to deliver cascades of hate messages directed both at individuals and communities. Thus addressing these issues has become a top priority for large-scale social media outlets. Three key challenges in automated detection and classification of hateful content are the lack of clearly labeled data, evolving vocabulary and lexicon - hashtags, emojis, etc. - and the lack of baseline models for fringe outlets such as Gab. In this work, we propose a novel framework with three major contributions. (a) We engineer an ensemble of deep learning models that combines the strengths of state-of-the-art approaches, (b) we incorporate a tuning factor into this framework that leverages transfer learning to conduct automated hate speech classification on unlabeled datasets, like Gab, and (c) we develop a weak supervised learning methodology that allows our framework to train on unlabeled data. Our ensemble models achieve an 83% hate recall on the HON dataset, surpassing the performance of the state-of-the-art deep models. We demonstrate that weak supervised training in combination with classifier tuning significantly increases model performance on unlabeled data from Gab, achieving a hate recall of 67%.