MemeMind: A Large-Scale Multimodal Dataset with Chain-of-Thought Reasoning for Harmful Meme Detection

TL;DR

MemeMind introduces a large-scale multimodal dataset with Chain-of-Thought annotations for detecting harmful memes, and proposes MemeGuard, a reasoning-based detection framework that improves accuracy and interpretability.

Contribution

The paper provides the first large-scale harmful meme dataset with detailed reasoning annotations and a novel multimodal detection model leveraging Chain-of-Thought reasoning.

Findings

MemeGuard outperforms existing methods on MemeMind dataset.

The dataset enables fine-grained analysis of implicit meme content.

The approach enhances both detection accuracy and interpretability.

Abstract

As a multimodal medium combining images and text, memes frequently convey implicit harmful content through metaphors and humor, rendering the detection of harmful memes a complex and challenging task. Although recent studies have made progress in detection accuracy and interpretability, large-scale, high-quality datasets for harmful memes remain scarce, and current methods still struggle to capture implicit risks and nuanced semantics. Thus, we construct MemeMind, a large-scale harmful meme dataset. Aligned with the international standards and the context of internet, MemeMind provides detailed Chain-of-Thought (CoT) reasoning annotations to support fine-grained analysis of implicit intentions in memes. Based on this dataset, we further propose MemeGuard, a reasoning-oriented multimodal detection framework that significantly improves both the accuracy of harmful meme detection and the interpretability of model decisions. Extensive experimental results demonstrate that MemeGuard outperforms existing state-of-the-art methods on the MemeMind dataset, establishing a solid foundation for future research in harmful meme detection. The complete dataset and code will be released upon acceptance.