Internet memes, channels for humor, social commentary, and cultural
expression, are increasingly used to spread toxic messages. Studies on the
computational analyses of toxic memes have significantly grown over the past
five years, and the only three surveys on computational toxic meme analysis
cover only work published until 2022, leading to inconsistent terminology and
unexplored trends. Our work fills this gap by surveying content-based
computational perspectives on toxic memes, and reviewing key developments until
early 2024. Employing the PRISMA methodology, we systematically extend the
previously considered papers, achieving a threefold result. First, we survey
119 new papers, analyzing 158 computational works focused on content-based
toxic meme analysis. We identify over 30 datasets used in toxic meme analysis
and examine their labeling systems. Second, after observing the existence of
unclear definitions of meme toxicity in computational works, we introduce a new
taxonomy for categorizing meme toxicity types. We also note an expansion in
computational tasks beyond the simple binary classification of memes as toxic
or non-toxic, indicating a shift towards achieving a nuanced comprehension of
toxicity. Third, we identify three content-based dimensions of meme toxicity
under automatic study: target, intent, and conveyance tactics. We develop a
framework illustrating the relationships between these dimensions and meme
toxicities. The survey analyzes key challenges and recent trends, such as
enhanced cross-modal reasoning, integrating expert and cultural knowledge, the
demand for automatic toxicity explanations, and handling meme toxicity in
low-resource languages. Also, it notes the rising use of Large Language Models
(LLMs) and generative AI for detecting and generating toxic memes. Finally, it
proposes pathways for advancing toxic meme detection and interpretation.

通过系统的调查与综述，我们填补了对有毒模因的内容分析的备受争议的术语和未探索趋势的先前相关研究的空白。我们对 118 篇新论文进行了调查，分析了 158 项内容为基础的有毒模因分析工作。我们提出了对模因有毒性类型进行分类的新分类法，并鉴别了模因有毒性的三个内容维度：目标、意图和传达策略。研究还分析了一些关键挑战和最新趋势，如增强的跨模态推理、专家和文化知识的整合、自动毒性解释的需求以及在资源匮乏语言中处理模因有毒性。最后，我们提出了推进有毒模因检测和解析的路径。