Hate Speech Targets Detection in Parler using BERT
This addresses the problem of hate speech targeting on alternative social media platforms like Parler, which is incremental as it applies existing BERT methods to new data.
The paper tackles hate speech detection on Parler by developing a two-model pipeline using BERT with back-translation and data pre-processing, achieving improved results for identifying hate speech and classifying its targets to create a distribution of hate targets on the platform.
Online social networks have become a fundamental component of our everyday life. Unfortunately, these platforms are also a stage for hate speech. Popular social networks have regularized rules against hate speech. Consequently, social networks like Parler and Gab advocating and claiming to be free speech platforms have evolved. These platforms have become a district for hate speech against diverse targets. We present in our paper a pipeline for detecting hate speech and its targets and use it for creating Parler hate targets' distribution. The pipeline consists of two models; one for hate speech detection and the second for target classification, both based on BERT with Back-Translation and data pre-processing for improved results. The source code used in this work, as well as other relevant sources, are available at: https://github.com/NadavSc/HateRecognition.git