标题： MemeMind：一种用于有害模因检测的大规模多模态数据集，包含思维链推理 显示英文标题

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

摘要： 社交媒体的快速发展加剧了有害内容的传播。 有害的模因，结合了图像和文本，由于其隐含语义和复杂的多模态交互，给自动化检测带来了重大挑战。 尽管现有研究在检测准确性和可解释性方面取得了进展，但缺乏系统性、大规模、多样化和高度可解释的数据集，继续阻碍着该领域的进一步发展。 为解决这一差距，我们引入了MemeMind，这是一个具有科学严谨标准、大规模、多样性、双语支持（中文和英文）以及详细思维链（CoT）注释的新数据集。 MemeMind通过提供全面的标注和明确的推理轨迹，填补了当前数据集的关键空白，从而为提高有害模因检测提供了坚实的基础。 此外，我们提出了一种创新的检测框架MemeGuard，它能够有效地将多模态信息与推理过程建模相结合，显著提高了模型理解和识别有害模因的能力。 在MemeMind数据集上进行的大量实验表明，MemeGuard在有害模因检测任务中始终优于现有的最先进方法。 显示英文摘要

摘要： The rapid development of social media has intensified the spread of harmful content. Harmful memes, which integrate both images and text, pose significant challenges for automated detection due to their implicit semantics and complex multimodal interactions. Although existing research has made progress in detection accuracy and interpretability, the lack of a systematic, large-scale, diverse, and highly explainable dataset continues to hinder further advancement in this field. To address this gap, we introduce MemeMind, a novel dataset featuring scientifically rigorous standards, large scale, diversity, bilingual support (Chinese and English), and detailed Chain-of-Thought (CoT) annotations. MemeMind fills critical gaps in current datasets by offering comprehensive labeling and explicit reasoning traces, thereby providing a solid foundation for enhancing harmful meme detection. In addition, we propose an innovative detection framework, MemeGuard, which effectively integrates multimodal information with reasoning process modeling, significantly improving models' ability to understand and identify harmful memes. Extensive experiments conducted on the MemeMind dataset demonstrate that MemeGuard consistently outperforms existing state-of-the-art methods in harmful meme detection tasks.