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Social media has seen a worrying rise in hate speech in recent times.Branching to several distinct categories of cyberbullying, genderdiscrimination, or racism, the combined label for such derogatory content canbe classified as toxic content in general. This paper presents experimentationwith a Keras wrapped lightweight BERT model to successfully identify hatespeech and predict probabilistic impact score for the same to extract thehateful words within sentences. The dataset used for this task is the HateSpeech and Offensive Content Detection (HASOC 2021) data from FIRE 2021 inEnglish. Our system obtained a validation accuracy of 82.60%, with a maximumF1-Score of 82.68%. Subsequently, our predictive cases performed significantlywell in generating impact scores for successful identification of the hatetweets as well as the hateful words from tweet pools.

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