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

arXiv: https://arxiv.org/abs/2111.12939 · HTML: https://mlsanity.com/paper/2111.12939/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 25 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Detecting Online Hate Speech Using Context Aware Models](https://mlsanity.com/paper/1710.07395/) (2017)
- [ktrain: A Low-Code Library for Augmented Machine Learning](https://mlsanity.com/paper/2004.10703/) (2020)

## Built on by (most cited first, 1 of 1)

- [Hypothesis Engineering for Zero-Shot Hate Speech Detection](https://mlsanity.com/paper/2210.00910/) (2022)

## Abstract (arXiv)

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.