Meme Datasets

Meme datasets are collections of multimodal data (images and text) used to train and evaluate machine learning models for understanding and classifying memes. Current research focuses on developing robust methods for identifying genuine memes, improving multimodal model architectures (like those incorporating transformers and CLIP) for tasks such as hate speech detection and meme classification, and creating more comprehensive datasets that account for the dynamic and contextual nature of memetics. This work is significant for advancing our understanding of online communication, enabling more effective content moderation, and providing valuable resources for social science research.