The automatic identification of harmful content online is of major concern
for social media platforms, policymakers, and society. Researchers have studied
textual, visual, and audio content, but typically in isolation. Yet, harmful
content often combines multiple modalities, as in the case of memes, which are
of particular interest due to their viral nature. With this in mind, here we
offer a comprehensive survey with a focus on harmful memes. Based on a
systematic analysis of recent literature, we first propose a new typology of
harmful memes, and then we highlight and summarize the relevant state of the
art. One interesting finding is that many types of harmful memes are not really
studied, e.g., such featuring self-harm and extremism, partly due to the lack
of suitable datasets. We further find that existing datasets mostly capture
multi-class scenarios, which are not inclusive of the affective spectrum that
memes can represent. Another observation is that memes can propagate globally
through repackaging in different languages and that they can also be
multilingual, blending different cultures. We conclude by highlighting several
challenges related to multimodal semiotics, technological constraints, and
non-trivial social engagement, and we present several open-ended aspects such
as delineating online harm and empirically examining related frameworks and
assistive interventions, which we believe will motivate and drive future
research.

该综述以有害网络迷因为研究对象，提出了新的分类方法，调查了相关研究现状，发现现有的数据集存在问题，迷因可通过多种语言进行传播，涉及的多模态语言和技术难题需要面对。