They Said Memes Were Harmless-We Found the Ones That Hurt: Decoding Jokes, Symbols, and Cultural References

TL;DR

This paper presents CROSS-ALIGN+, a three-stage framework that improves meme-based social abuse detection by incorporating cultural knowledge, refining decision boundaries, and providing interpretability, outperforming existing methods across multiple benchmarks.

Contribution

The paper introduces CROSS-ALIGN+, a novel framework that systematically addresses cultural blindness, boundary ambiguity, and interpretability in meme abuse detection.

Findings

Up to 17% relative F1 improvement over state-of-the-art methods.

Effective integration of structured cultural knowledge enhances detection accuracy.

Provides interpretable justifications for each decision.

Abstract

Meme-based social abuse detection is challenging because harmful intent often relies on implicit cultural symbolism and subtle cross-modal incongruence. Prior approaches, from fusion-based methods to in-context learning with Large Vision-Language Models (LVLMs), have made progress but remain limited by three factors: i) cultural blindness (missing symbolic context), ii) boundary ambiguity (satire vs. abuse confusion), and iii) lack of interpretability (opaque model reasoning). We introduce CROSS-ALIGN+, a three-stage framework that systematically addresses these limitations: (1) Stage I mitigates cultural blindness by enriching multimodal representations with structured knowledge from ConceptNet, Wikidata, and Hatebase; (2) Stage II reduces boundary ambiguity through parameter-efficient LoRA adapters that sharpen decision boundaries; and (3) Stage III enhances interpretability by generating cascaded explanations. Extensive experiments on five benchmarks and eight LVLMs demonstrate that CROSS-ALIGN+ consistently outperforms state-of-the-art methods, achieving up to 17% relative F1 improvement while providing interpretable justifications for each decision.