  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.
