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Improving Hate Speech Detection with Deep Learning Ensembles

Hate speech has become a major issue that is currently a hot topic in the domain of social media. Simultaneously, current proposed methods to address the issue raise concerns about censorship. Broadly speaking, our research focus is the area human rights, including the development of new methods to identify and better address discrimination while protecting freedom of expression. As neural network approaches are becoming state of the art for text classification problems, an ensemble method is adapted for usage with neural networks and is presented to better classify hate speech. Our method utilizes a publicly available embedding model, which is tested against a hate speech corpus from Twitter. To confirm robustness of our results, we additionally test against a popular sentiment dataset. Given our goal, we are pleased that our method has a nearly 5 point improvement in F-measure when compared to original work on a publicly available hate speech evaluation dataset. We also note difficulties encountered with reproducibility of deep learning methods and comparison of findings from other work. Based on our experience, more details are needed in published work reliant on deep learning methods, with additional evaluation information a consideration too. This information is provided to foster discussion within the research community for future work.