BiCapsHate: Attention to the Linguistic Context of Hate via Bidirectional Capsules and Hatebase

Online social media (OSM) communications sometimes turn into hate-filled and offensive comments or arguments. It not just disrupts the social fabric online, but also leads to hate, violence, and crime, in the real physical world in worst scenarios. The existing content moderation practices of OSM platforms often fail to control the online hate. In this article, we develop a deep learning model called
BiCapsHate
to detect hate speech (HS) in OSM posts. The model consists of five layers of deep neural networks. It starts with an input layer to process the input text and follows on to an embedding layer to embed the text into a numeric representation. A BiCaps layer then learns the sequential and linguistic contextual representations, a dense layer prepares the model for final classification, and lastly the output layer produces the resulting class as either hate or non-HS (NHS). The BiCaps layer, being the most important component, effectively learns the contextual information with respect to different orientations in both forward and backward directions of the input text via capsule networks. It is further aided by our rich set of hand-crafted shallow and deep auxiliary features including the
Hatebase
lexicon, making the model well-informed. We conduct extensive experiments on five benchmark datasets to demonstrate the efficacy of the proposed
BiCapsHate
model. In the overall results, we outperform the existing state-of-the-art methods including fBERT, HateBERT, and ToxicBERT.
BiCapsHate
achieves up to 94% and 92% f-score on balanced and imbalanced datasets, respectively. Our complete source code is publicly available at GitHub repository
https://github.com/Ashraf-Kamal/BiCapsHate
.