D-HUMOR: Dark Humor Understanding via Multimodal Open-ended Reasoning - A Benchmark Dataset and Method

• URL: http://arxiv.org/abs/2509.06771v2
• Date: Thu, 30 Oct 2025 10:15:05 GMT
• Title: D-HUMOR: Dark Humor Understanding via Multimodal Open-ended Reasoning - A Benchmark Dataset and Method
• Authors: Sai Kartheek Reddy Kasu, Mohammad Zia Ur Rehman, Shahid Shafi Dar, Rishi Bharat Junghare, Dhanvin Sanjay Namboodiri, Nagendra Kumar,
• Abstract summary: Dark humor in online memes poses unique challenges due to its reliance on implicit, sensitive, and culturally contextual cues.<n>We introduce a novel dataset of 4,379 memes annotated for dark humor, target category (gender, mental health, violence, race, disability, and other), and a three-level intensity rating.<n>We propose a reasoning-augmented framework that first generates structured explanations for each meme using a Large Vision-Language Model.
• Score: 4.561044673225099
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dark humor in online memes poses unique challenges due to its reliance on implicit, sensitive, and culturally contextual cues. To address the lack of resources and methods for detecting dark humor in multimodal content, we introduce a novel dataset of 4,379 Reddit memes annotated for dark humor, target category (gender, mental health, violence, race, disability, and other), and a three-level intensity rating (mild, moderate, severe). Building on this resource, we propose a reasoning-augmented framework that first generates structured explanations for each meme using a Large Vision-Language Model (VLM). Through a Role-Reversal Self-Loop, VLM adopts the author's perspective to iteratively refine its explanations, ensuring completeness and alignment. We then extract textual features from both the OCR transcript and the self-refined reasoning via a text encoder, while visual features are obtained using a vision transformer. A Tri-stream Cross-Reasoning Network (TCRNet) fuses these three streams, text, image, and reasoning, via pairwise attention mechanisms, producing a unified representation for classification. Experimental results demonstrate that our approach outperforms strong baselines across three tasks: dark humor detection, target identification, and intensity prediction. The dataset, annotations, and code are released to facilitate further research in multimodal humor understanding and content moderation. Code and Dataset are available at: https://github.com/Sai-Kartheek-Reddy/D-Humor-Dark-Humor-Understanding-via-Multimodal-Open-ended-Rea soning

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