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.

翻译：本文着重探讨在网上社交媒体上发现攻击性类比模式的重要问题,即视觉内容和Meme文本/内容合在一起作为传递攻击性信息的一种类比;现有的攻击性微量探测解决方案往往忽视Meme的视觉内容和文字内容之间的隐含关系,不足以确定攻击性类比模式;在准确检测攻击性类比模式方面存在两大挑战:(一) 获取通常由memee暗含的类比并非微不足道;(二) 有效地将复杂类比与一个memee的不同数据模式相匹配也是一项挑战。为了应对上述挑战,我们开发了一个深层次的学习框架,以模拟-敏锐攻击性Meme探测(AOMD)为基础,从Meme的多模式内容中学习隐含的类比,并有效检测攻击性类比 memes。我们根据网上社交媒体的两个真实世界数据集对AOMD进行了评估。评价结果表明,AMD通过更准确地探测攻击性类比Memes,取得了显著的业绩收益。