D-HUMOR: Dark Humor Understanding via Multimodal Open-ended Reasoning
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-Reasoning
68bf971c207285de11b07b9d
Sai Kartheek Reddy Kasu
claimed_verified
2025-09-09T13:47:52.150Z
651692d718f3a57f869a5a0a
/avatars/d5fe48de11e46675b05e1e2e1cf3505c.svg
Sai Kartheek Reddy
user
UVSKKR
68bf971c207285de11b07b9e
Mohammad Zia Ur Rehman
68bf971c207285de11b07b9f
Shahid Shafi Dar
68bf971c207285de11b07ba0
Rishi Bharat Junghare
68bf971c207285de11b07ba1
Dhanvin Sanjay Namboodiri
68bf971c207285de11b07ba2
Nagendra Kumar
D-HUMOR: Dark Humor Understanding via Multimodal Open-ended Reasoning
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-Reasoning
A reasoning-augmented framework using a Large Vision-Language Model and a Tri-stream Cross-Reasoning Network achieves superior performance in detecting dark humor, identifying targets, and predicting intensity in multimodal memes.
Large Vision-Language Model Role-Reversal Self-Loop text encoder vision transformer Tri-stream Cross-Reasoning Network pairwise attention mechanisms