Friday 14 March 2025
The quest for a better understanding of internet memes has led researchers to develop novel approaches in natural language processing and multimodal learning. In recent years, the study of memes has gained significant attention due to their widespread popularity on social media platforms and their ability to convey complex ideas and emotions.
To tackle the challenge of meme comprehension, a team of researchers has proposed a dataset, called ClassicMemes-50 (CM50), which consists of 33,000 labelled memes. The dataset is designed to facilitate the development of multimodal models that can analyze and generate memes effectively. CM50 includes three types of captions: image captions, meme captions, and embedded text, along with their corresponding literary device labels.
The researchers have also developed a novel prompt engineering framework for meme annotation. This framework enables the creation of high-quality prompts that can elicit accurate literary device labels from models. The framework consists of three main components: a baseline prompt, a few-shot learning task, and a three-step reasoning prompt. Each component is designed to improve the model’s understanding of memes by providing more context and guidance.
The team has tested their approach on two datasets, Figmemes and Memecap, which are widely used in meme research. The results show that the proposed framework can significantly improve the accuracy of literary device labeling tasks. The macro F1-score, a commonly used metric for evaluating classification models, increased by 10% to 15% compared to baseline models.
The CM50 dataset and prompt engineering framework have several potential applications in natural language processing and multimodal learning. For instance, they can be used to develop more accurate meme generators that can create humorous content based on user input. Additionally, the datasets and prompts can facilitate the development of more effective meme detectors that can identify and classify memes accurately.
The study’s findings also highlight the importance of multimodal learning in natural language processing. The combination of image captions, meme captions, and embedded text provides a richer understanding of memes than relying solely on text-based inputs. This approach can be applied to other areas of research where multimodal data is available, such as sentiment analysis and question answering.
In addition to its technical contributions, the study demonstrates the potential for interdisciplinary research between computer science, linguistics, and cognitive psychology. The development of more accurate meme comprehension models requires a deep understanding of human language and cognition, as well as the ability to analyze and generate multimodal content.
Cite this article: “Unlocking Meme Comprehension through Novel Approaches in Natural Language Processing and Multimodal Learning”, The Science Archive, 2025.
Internet Memes, Natural Language Processing, Multimodal Learning, Meme Comprehension, Classicmemes-50, Dataset, Literary Devices, Prompt Engineering, Meme Annotation, F1-Score
