用多模态模型识别带伊斯兰恐惧症的讽刺表情包。
MIMIC: Multimodal Islamophobic Meme Identification and Classification
- 结合图文特征的ViLT模型捕捉隐含歧视内容
- 在自建数据集上达到高检测准确率
- 适合研究网络仇恨言论与文化语境者
针对以图片和文字构成、看似幽默实则传递伊斯兰恐惧情绪的表情包中的反穆斯林仇恨言论，本文提出一个新数据集，并构建基于视觉-语言变换器（ViLT）的分类器。该模型通过联合编码表情包图像与文本信息，捕捉膜文化中特有的细微歧视叙事，实现高精度检测并具备良好可扩展性。实验表明，该方法能有效识别伪装成幽默的隐性反穆斯林内容，为社交媒体安全提供技术支撑。
原文摘要 · Abstract (English)
Anti-Muslim hate speech has emerged within memes, characterized by context-dependent and rhetorical messages using text and images that seemingly mimic humor but convey Islamophobic sentiments. This work presents a novel dataset and proposes a classifier based on the Vision-and-Language Transformer (ViLT) specifically tailored to identify anti-Muslim hate within memes by integrating both visual and textual representations. Our model leverages joint modal embeddings between meme images and incorporated text to capture nuanced Islamophobic narratives that are unique to meme culture, providing both high detection accuracy and interoperability.
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