Computational Meme Understanding, which concerns the automated comprehension of memes, has garnered interest over the last four years and is facing both substantial opportunities and challenges. We survey this emerging area of research by first introducing a comprehensive taxonomy for memes along three dimensions – forms, functions, and topics. Next, we present three key tasks in Computational Meme Understanding, namely, classification, interpretation, and explanation, and conduct a comprehensive review of existing datasets and models, discussing their limitations. Finally, we highlight the key challenges and recommend avenues for future work.

While recent years have seen a surge of interest in the automatic processing of memes, much of the work in this area has focused on determining whether a meme contains malicious content. This paper proposes the new task of intent description generation: generating a description of the author’s intentions when creating the meme. To stimulate future work on this task, we (1) annotated a corpus of memes with the intents being perceived by the reader as well as the background knowledge needed to infer the intents and (2) established baseline performance on the intent description generation task using state-of-the-art large language models. Our results suggest the importance of background knowledge retrieval in intent description generation for memes.