MemeSense: An Adaptive In-Context Framework for Social Commonsense Driven Meme Moderation

TMLR Paper5318 Authors

Abstract:

Online memes are a powerful yet challenging medium for content moderation, often masking harmful intent behind humor, irony, or cultural symbolism. Conventional moderation systems “especially those relying on explicit text” frequently fail to recognize such subtle or implicit harm. We introduce MemeSense, an adaptive framework designed to generate socially grounded interventions for harmful memes by combining visual and textual understanding with curated, semantically aligned examples enriched with commonsense cues. This enables the model to detect nuanced complexed threats like misogyny, stereotyping, or vulgarity “even in memes lacking overt language”. Across multiple benchmark datasets, MemeSense outperforms state-of-the-art methods, achieving up to 35% higher semantic similarity
and 9% improvement in BERTScore for non-textual memes, and notable gains for text-rich memes as well. These results highlight MemeSense as a promising step toward safer, more context-aware AI systems for real-world content moderation. The code is available at: https://anonymous.4open.science/r/MemeSense/

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Gunhee_Kim1

Submission Number: 5318