This paper focuses on an important problem of detecting offensive analogy meme on online social media where the visual content and the texts/captions of the meme together make an analogy to convey the offensive information. Existing offensive meme detection solutions often ignore the implicit relation between the visual and textual contents of the meme and are insufficient to identify the offensive analogy memes. Two important challenges exist in accurately detecting the offensive analogy memes: i) it is not trivial to capture the analogy that is often implicitly conveyed by a meme; ii) it is also challenging to effectively align the complex analogy across different data modalities in a meme. To address the above challenges, we develop a deep learning based Analogy-aware Offensive Meme Detection (AOMD) framework to learn the implicit analogy from the multi-modal contents of the meme and effectively detect offensive analogy memes. We evaluate AOMD on two real-world datasets from online social media. Evaluation results show that AOMD achieves significant performance gains compared to state-of-the-art baselines by detecting offensive analogy memes more accurately.

本文侧重于检测的重要课题进攻比喻米姆在线社交媒体在视觉内容和米姆的文本/字幕一起组成一个比喻要传达的信息攻势。现有的攻击性模因检测解决方案往往忽略了模因的视觉和文本内容之间的隐含关系，不足以识别令人反感的类比模因。准确检测令人反感的类比模因存在两个重要挑战：i）捕捉模因通常隐含地传达的类比并非易事；ii）其也具有挑战性的有效对准跨不同数据模式的复杂类似于在模因。为了应对上述挑战，我们开发了一个深刻的学习基于类比感知进攻米姆检测（AOMD）学习的框架从梅梅的多模态内容隐含比喻和有效地检测攻击的比喻记因。我们评估从在线社交媒体两个真实世界的数据集AOMD。