  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.
