| Challenge: | In this paper, we analyze memes as a form of language subject to the same kinds of sociolinguistic variation as other modalities, such as written language and speech. |
| Approach: | They propose a computational pipeline to cluster memes into templates and semantic variables, taking advantage of their multimodal structure to learn meme semantics from an unstructured dataset. |
| Outcome: | The proposed method uses 3.8M images from a reddit meme database to analyze linguistic variation in memes. |
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| Challenge: | Existing studies characterizing language variation across Internet social groups have focused on the types of words used by these groups. |
| Approach: | They extend this study by employing BERT to characterize variation in the senses of words as well, analyzing two months of English comments in 474 Reddit communities. |
| Outcome: | The proposed study analyzes two months of English comments in 474 Reddit communities and ties language variation with community behavior. |
Computational Meme Understanding: A Survey (2024.emnlp-main)
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| Challenge: | Computational Meme Understanding (CMU) is a collection of tasks involving the automated comprehension of memes. |
| Approach: | They propose a comprehensive taxonomy for memes along three dimensions – forms, functions, and topics and introduce three key tasks for Computational Meme Understanding, namely classification, interpretation, and explanation. |
| Outcome: | The proposed model is based on a taxonomy of memes along three dimensions and is compared to existing models and datasets. |
MemoSen: A Multimodal Dataset for Sentiment Analysis of Memes (2022.lrec-1)
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| Challenge: | Recent studies on sentiment analysis of memes have focused on English, but there is a significant barrier to performing multimodal sentiment analysis research in resource-constrained languages like Bengali. |
| Approach: | They propose to use a Bengali dataset to perform multimodal sentiment analysis in low resource languages. |
| Outcome: | The proposed dataset for Bengali contains 4417 memes with three annotated labels positive, negative, and neutral. |
MetaMeme: A Dataset for Meme Template and Meta-Category Classification (2025.naacl-srw)
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| Challenge: | a new dataset for classifying memes by their template and communicative intent is presented. |
| Approach: | They propose a new dataset for classifying memes by their template and communicative intent. |
| Outcome: | The proposed method outperforms existing methods in classifying memes by their template and communicative intent. |
FigMemes: A Dataset for Figurative Language Identification in Politically-Opinionated Memes (2022.emnlp-main)
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| Challenge: | FigMemes is a dataset for figurative language classification in politically-opinionated memes. |
| Approach: | They propose to use figurative language classification to identify politically-opinionated memes by analyzing their datasets and comparing them to other machine learning models. |
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Variationist: Exploring Multifaceted Variation and Bias in Written Language Data (2024.acl-demos)
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| Challenge: | Existing tools that inspect and visualize language data are limited in their capabilities. |
| Approach: | They propose a highly-modular, extensible, and task-agnostic tool that inspects language variation and bias across multiple variables, language units, and diverse metrics. |
| Outcome: | The proposed tool can inspect and visualize language variation and bias across variables, language units, and diverse metrics that go beyond descriptive statistics. |
MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization (2023.acl-long)
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| Challenge: | Besides digital archiving of memes and their metadata, there is no efficient way to deduce a meme’s context dynamically. |
| Approach: | They propose a task to mine the context that succinctly explains the background of a meme and a related document to capture cross-modal semantic dependencies between the meme and the context. |
| Outcome: | The proposed dataset outperforms existing systems and shows that it can capture cross-modal semantic dependencies between the meme and the context. |
Meme-ingful Analysis: Enhanced Understanding of Cyberbullying in Memes Through Multimodal Explanations (2024.eacl-long)
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| Challenge: | Recent laws like “right to explanations” have spurred research in developing interpretable models . a recent study has shown that multimodal explanations improve performance in generating textual justifications . |
| Approach: | They propose to use visual and textual modalities to explain why a given meme is cyberbullying . they use a Contrastive Language-Image Pretraining approach to generate textual justifications . |
| Outcome: | The proposed model improves performance in visual and textual explanations and identifies the visual evidence supporting a decision. |
MemeCap: A Dataset for Captioning and Interpreting Memes (2023.emnlp-main)
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| Challenge: | a new dataset aims to understand meme captioning tasks using visual metaphors . vision and language models are proving to be effective in image captioning and visual question answering tasks . |
| Approach: | They present a dataset that contains 6.3K memes and 6.3k meme captions . they show that vision and language models still struggle with visual metaphors despite their advanced capabilities . |
| Outcome: | The proposed dataset contains 6.3K memes along with the title of the post containing the meme, meme captions, literal image caption, and visual metaphors. |
Are Large Language Models Chronically Online Surfers? A Dataset for Chinese Internet Meme Explanation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are trained on vast amounts of text from the Internet, but do they understand the viral content that rapidly spreads online? |
| Approach: | They introduce a dataset for CHinese Internet Meme Explanation that includes popular phrase-based memes from the Chinese Internet. |
| Outcome: | The proposed dataset includes popular phrase-based memes from the Chinese Internet, annotated with detailed information on their meaning, origin, example sentences, types, etc. |