MemeMatch: A Large-Scale Dual-Context Multimodal Dataset and Retrieval System for Internet Memes - Le, Do Tri An; Köller, Donát Ákos; Deng, Qixin; Molontay, Roland (Proceedings of the International AAAI Conference on Web and Social Media, 2026)
We introduce MemeMatch, a large-scale multimodal meme dataset and retrieval system that bridges meme collection, annotation, and analysis in a unified pipeline. The dataset contains nearly one million image-with-text memes from Reddit’s r/Memes (2018–2023) and ImgFlip, with rich metadata. Each meme is decomposed into two semantic contexts: local context, capturing the editable text payload (overlay text and title), and global context, capturing the underlying visual substrate or template semantics. Both are enriched with transformer-based annotations, including 14-dimensional sentiment and emotion vectors, BERTopic-derived topics, and zero-shot usage-intent labels. This structured representation supports exploratory analysis and context-aware retrieval by natural language or image query.