In order to better detect hidden hate comments or posts, the paper introduces sarcasm-based rationale (emoticons or text segments that indicate sarcasm) combined with hate/offensive rationale. HateXplain, an established benchmark hate dataset, was used to create a dataset by labeling texts and choosing justifications based on sarcasm. Then, the recently created dataset was used with the current cutting-edge model. When sarcasm rationale was combined with hate/offensive rationale in a newly formed attention proposed in the data’s preprocessing step, the model’s F1-score increased by 0.01. Furthermore, a noteworthy enhancement was noted in explainability metrics like faithfulness and plausibility with the new data. https://www.mdpi.com/2076-3417/14/11/4898 Share this: Print (Opens in new window) Print Share on Facebook (Opens in new window) Facebook Share on LinkedIn (Opens in new window) LinkedIn Share on Reddit (Opens in new window) Reddit Share on WhatsApp (Opens in new window) WhatsApp Share on Bluesky (Opens in new window) Bluesky Email a link to a friend (Opens in new window) Email Like this:Like Loading... Post navigation Two Weeks in P/CVE: Free Resources on Countering Extremism and Hate, May 2024 (II/II) Two Weeks in P/CVE: Free Resources on Countering Extremism and Hate, June 2024 (I/II)