Clustering Internet Memes Through Template Matching and Multi-Dimensional Similarity
This work addresses meme clustering for applications like toxicity detection and virality modeling, representing a domain-specific advancement in an understudied area.
The paper tackles the problem of clustering Internet memes, which is challenging due to their multimodality and cultural context, by introducing a template-based matching method with multi-dimensional similarity features, eliminating the need for predefined databases and outperforming existing clustering methods.
Meme clustering is critical for toxicity detection, virality modeling, and typing, but it has received little attention in previous research. Clustering similar Internet memes is challenging due to their multimodality, cultural context, and adaptability. Existing approaches rely on databases, overlook semantics, and struggle to handle diverse dimensions of similarity. This paper introduces a novel method that uses template-based matching with multi-dimensional similarity features, thus eliminating the need for predefined databases and supporting adaptive matching. Memes are clustered using local and global features across similarity categories such as form, visual content, text, and identity. Our combined approach outperforms existing clustering methods, producing more consistent and coherent clusters, while similarity-based feature sets enable adaptability and align with human intuition. We make all supporting code publicly available to support subsequent research.