Mapping Memes to Words

• Mapping Memes to Words is defined as the computational process of translating composite visual templates and text overlays into structured, culturally embedded semantic labels.
• The methodology employs multimodal embeddings, template-based classification, and automated annotation pipelines to enhance retrieval, captioning, and sociolinguistic analysis.
• Practical applications include meme classification, content moderation, and cultural analytics, leveraging structured knowledge graphs and robust evaluation metrics.

Memes are multimodal artifacts that intertwine visual templates and text overlays to convey complex, often humorous, culturally embedded meanings. The problem of mapping memes to words entails translating these composite, context-sensitive visuals into rich, interpretable semantic, lexical, or textual representations. Research in computational meme analysis addresses this challenge through template-centric representations, multimodal embeddings, prompt-driven annotation, and pipelines that integrate social and communicative context. These methodological advances support a range of applications from classification and retrieval to automated explanation, moderation, and sociolinguistic exploration.

1. Template Structure and Semantic Foundations

A central insight is that most memes are constructed from a small set of widely-recognized templatic images whose base semantics carry stable, community-endorsed meaning. Each meme instance customizes this "base template" with overlay text or visual perturbations, producing variation while retaining a core schema ("twofold" structure: invariant template + variable overlay) (Bates et al., 2023). Knowledge bases such as the Know Your Meme Knowledge Base (KYMKB) catalog thousands of such base templates, enriching each with "about" sections, origin stories, and canonical example instances. These repositories bridge the gap between raw meme images and the "word-like" cultural labels that typify meme functions and usage, formalizing mappings such as

$$\text{Assigned label} = \arg\max_{\text{ref}} \operatorname{count}(\operatorname{rank}(\text{ref}, \text{query}_{\text{train}}))$$

where "ref" is from the template vocabulary and "query_train" are embeddings of training instances (Bates et al., 2023). This approach operationalizes the template-label counter for efficient, nonparametric meme classification.

2. Multimodal Embeddings and Decoupling Techniques

Mapping meme content to semantic space necessitates extracting and aligning information from both the visual template and the text overlay. Early work introduces sparse coding algorithms that decouple base image content from the noisy overlays via $\ell^1$-minimization, producing semantic embeddings that concatenate deep image features (e.g., from CNNs) and OCR-extracted text representations (Dubey et al., 2018). The resulting feature vectors

$v_i = [f(o_i),\; f(t_i-o_i)]$

where $f$ is a feature extractor, $o_i$ is the decoupled template, and $t_i$ is the meme image, enable robust clustering, retrieval, topic, and virality prediction.

Later methods adopt joint embedding spaces using vision-LLMs—CLIP, StanJE, and transformer-based encoders—to project images, templates, and text (including OCR) into aligned vector spaces, often minimizing distance or triplet loss for paired data (Gunti et al., 2021). Finer-grained methods map images to the semantics of individual words (word-image joint embedding), enhancing the representation's relational and analogical properties.

Textual inversion (as in ISSUES (Burbi et al., 2023)) goes further by learning a pseudo-word token in the CLIP embedding space from visual inputs, enabling a direct text-domain mapping. This supports multimodal fusion strategies and enriches the expressiveness of downstream classifiers.

3. Automated Annotation and Captioning Pipelines

Scaling semantic annotation and bridging modality gaps require automated pipelines with advanced language and vision models. Knowledge-grounded pipelines prompt large vision-LLMs (VLMs) such as GPT-4o (or LLaVA variants) to generate image captions, meme captions (intended meaning), literary device labels, and OCR text in a stepwise fashion (Deng et al., 23 Jan 2025). For each meme:

• The VLM first "explains" the meme, synthesizing image and text data.
• It produces both a literal image caption and an interpretation-focused meme caption.
• Literary device annotation is performed with supplied definitions to capture figurative elements (irony, allusion, etc.).

These representations are used both for dataset construction (e.g., CM50 (Deng et al., 23 Jan 2025)) and as dense anchors for cross-modal retrieval models, such as mtrCLIP, which optimize cosine similarity between normalized image and text embeddings:

$s(v, t) = \frac{v \cdot t}{\|v\| \|t\|}$

Automated pipelines not only offer scalable alternatives to manual annotation but also propagate knowledge from structured meme databases and prompt engineering.

4. Contextual, Sociolinguistic, and Reasoning-Aware Models

Meme interpretation is deeply context-sensitive—meaning is shaped by community, temporal, and conversational cues. Computational frameworks account for this in several ways:

• Sociolinguistic clustering pipelines, like SemanticMemes, first group meme images into templates (using perceptual hashes and Leiden clustering) and then into higher-order semantic clusters using fine-tuned text (RoBERTa) and multimodal (CLIP) embeddings. This enables the analysis of linguistic variation, subcommunity-specific innovations, and the spread of meme "dialects" across subreddits (Zhou et al., 2023).
• Benchmarks such as MemeReaCon combine meme image, post text, and user comments from online communities to evaluate how models integrate multimodal and social context. Tasks include classifying context-meme interplay, generating free-form explanations, and inferring post intent, all quantitatively evaluated with scores such as the Context Relevance Score (CRS) (2505.17433). These reveal a "performance cliff" in LVLMs when moving from surface-level tasks to deeper, context-integrating inference.
• Moderation-focused frameworks (e.g., MemeSense) fuse visual, lexical, and social commonsense signals. Through multi-stage pipelines—generating commonsense parameters, selecting in-context exemplars, and computing cognitive shift vectors ($$M_{\text{ivt}}^{(\ell)} = M^{(\ell)} + \alpha^{(\ell)} \cdot csv^{(\ell)}$$)—they adapt model representations to implicitly harmful or contextually sensitive content (Adak et al., 16 Feb 2025).

5. Knowledge Graphs and Structured Representations

Some approaches formalize meme content via scene graphs and knowledge graphs, extracting visual objects and their relations (scene graphs) and linking text entities to external background knowledge (e.g., from Wikidata) (Kougia et al., 2023, Joshi et al., 2023). The method serializes both (e.g., triplets like "man wearing shirt;" KB lookups) and concatenates them into a unified text sequence for language-model-based classification. This hybrid strategy uses structural and semantic grounding to tackle tasks such as hatefulness detection and contextualization.

Knowledge graphs such as IMKG introduce hierarchical organization (media frames, templates, instances) and integrate third-party metadata (e.g., KnowYourMeme tags, Wikidata entities), allowing for robust mapping of meme images from social media platforms to rich semantic context (Joshi et al., 2023).

6. Evaluation Metrics and Benchmarks

The efficacy of multimodal meme-to-word mapping is established via several metrics:

• Retrieval metrics (e.g., Recall@K) assess the alignment between meme images and textual annotations (Deng et al., 23 Jan 2025).
• Classification and generative metrics (accuracy, macro-F1, BLEU, ROUGE-L, METEOR, BERTScore) evaluate performance on meme captioning, explanation generation, context-meme interplay labeling, and question answering (2305.13703, Sharma et al., 2022, Abaskohi et al., 3 Apr 2024, Agarwal et al., 18 May 2024, 2505.17433).
• Hierarchical metrics (hierarchical precision, recall, F1) quantify the compatibility of predictions with complex label structures (Abaskohi et al., 3 Apr 2024).
• Downstream analyses track model generalization, robustness to question or label variation, and cultural adaptation.

Experimental results consistently show that integrating template knowledge, context, and structured annotations improves interpretability and classification accuracy, often surpassing baselines that treat memes as undifferentiated multimodal inputs.

7. Limitations and Future Directions

Current models contend with modality gap, metaphorical and abstract visual semantics, cultural specificity, and context-dependent intent. Studies reveal that vision encoders may miss metaphor or irony, that multimodal fusion can falter in ambiguous settings, and that large models may still focus too much on one modality to the detriment of holistic interpretation (2305.13703, Abaskohi et al., 3 Apr 2024, 2505.17433).

Moving forward, research emphasizes:

• Improved in-context learning, chain-of-thought, and reasoning augmentation.
• Enhanced pre-training on culturally and contextually diverse datasets.
• Advanced methods for fusing modalities and incorporating social, ethical, and commonsense reasoning.
• Flexible systems capable of real-time adaptation to novel or adversarial meme trends.

A plausible implication is that effective mapping of memes to words will continue to require not only technical advances in AI architectures but also deep integration of community, cultural, and discourse-aware context.

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These research trajectories collectively provide a comprehensive foundation for mapping the multimodal, dynamic content of memes to rich, structured verbal representations—enabling advances in automated meme interpretation, generation, moderation, retrieval, and cultural analytics.