Probabilistic Impact Score Generation using Ktrain-BERT to Identify Hate Words from Twitter Discussions

TL;DR

This paper introduces a Ktrain-BERT based approach to identify hate speech and generate impact scores for hateful words in Twitter discussions, achieving high accuracy on the HASOC 2021 dataset.

Contribution

It presents a novel application of lightweight Ktrain-BERT for hate speech detection and impact score prediction, improving interpretability of hateful content.

Findings

Validation accuracy of 82.60%

F1-Score of 82.68%

Effective identification of hateful words

Abstract

Social media has seen a worrying rise in hate speech in recent times. Branching to several distinct categories of cyberbullying, gender discrimination, or racism, the combined label for such derogatory content can be classified as toxic content in general. This paper presents experimentation with a Keras wrapped lightweight BERT model to successfully identify hate speech and predict probabilistic impact score for the same to extract the hateful words within sentences. The dataset used for this task is the Hate Speech and Offensive Content Detection (HASOC 2021) data from FIRE 2021 in English. Our system obtained a validation accuracy of 82.60%, with a maximum F1-Score of 82.68%. Subsequently, our predictive cases performed significantly well in generating impact scores for successful identification of the hate tweets as well as the hateful words from tweet pools.