SafeSpeech: a three-module pipeline for hate intensity mitigation of social media texts in Indic languages

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Identifying and mitigating hateful, abusive, offensive comments on social media is a crucial, paramount task. It’s challenging to entirely prevent such hateful content and impose rigorous censorship on social platforms while safeguarding free speech. Recent studies have focused on detecting hate speech, whereas mitigating the intensity of hate remains unexplored or somewhat complex. This paper introduces a cost-effective, straightforward, and novel three-module pipeline, SafeSpeech, for Hate Speech Classification, Hate Intensity Identification, and Hate Intensity Mitigation on social media texts. The initial module classifies text as either containing or not containing hate speech. Following this, the second module quantifies the intensity of hate associated with individual words within the classified hate speech. Lastly, the third module seeks to diminish the overall hatefulness conveyed in the text. A comprehensive experiment has been conducted using publicly available datasets in five Indic languages (Hindi, Marathi, Tamil, Telugu, and Bengali). The system undergoes thorough evaluation to assess its performance and analyze it in-depth using various automated metrics. We evaluated the performance of Hate Speech Classification using Precision, Recall, and F1 metrics. For Hate Intensity Identification, we use human evaluation. Recognizing the limitations of automated metrics in mitigating hate speech, we augment our experiments with human evaluation for Hate Intensity Mitigation, where three domain experts independently participated. BERTScore for final generated hate-mitigated texts and first classified hate texts across all languages consistently range between 0.96 and 0.99.