  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%.
