Hate Speech Detection by Using Rationales for Judging Sarcasm

Abstract

The growing number of social media users has impacted the rise in hate comments and posts. While extensive research in hate speech detection attempts to combat this phenomenon by developing new datasets and detection models, reconciling classification accuracy with broader decision-making metrics like plausibility and faithfulness remains challenging. As restrictions on social media tighten to stop the spread of hate and offensive content, users have adapted by finding new approaches, often camouflaged in the form of sarcasm. Therefore, dealing with new trends such as the increased use of emoticons (negative emoticons in positive sentences) and sarcastic comments is necessary. This paper introduces sarcasm-based rationale (emoticons or portions of text that indicate sarcasm) combined with hate/offensive rationale for better detection of hidden hate comments/posts. A dataset was created by labeling texts and selecting rationale based on sarcasm from the existing benchmark hate dataset, HateXplain. The newly formed dataset was then applied in the existing state-of-the-art model. The model’s F1-score increased by 0.01 when using sarcasm rationale with hate/offensive rationale in a newly formed attention proposed in the data’s preprocessing step. Also, with the new data, a significant improvement was observed in explainability metrics such as plausibility and faithfulness.