arXiv:2505.00056v1 Announce Type: cross
Abstract: 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. Code: https://github.com/tygobl/meme-clustering
Analyzing the Importance of Meme Clustering in Multimedia Information Systems
Clustering similar Internet memes is a crucial task in various areas such as toxicity detection, virality modeling, and typing. Despite its significance, meme clustering has received little attention in previous research. The complexity arises from the multimodality, cultural context, and adaptability of memes. However, a recent paper introduces a novel method that addresses these challenges and significantly improves the clustering process.
The Multidisciplinary Nature of Meme Clustering
Understanding meme clustering requires a multi-disciplinary approach that incorporates insights from various fields. In the context of multimedia information systems, memes are not only composed of text but also encompass visual content, form, and identity. Hence, an effective clustering method must consider these multiple dimensions of similarity to accurately group together similar memes.
Moreover, since memes are deeply rooted in cultural contexts, understanding the underlying semantics is crucial. The proposed method takes this into account and eliminates the reliance on predefined databases, allowing for adaptive matching. This approach ensures that the clustering process remains relevant and up-to-date as new memes emerge and cultural contexts evolve.
The Role of Multi-Dimensional Similarity Features
The innovative aspect of the proposed method lies in its use of multi-dimensional similarity features. By considering local and global features across different similarity categories, such as form, visual content, text, and identity, the clustering algorithm achieves superior performance compared to existing methods. This multi-dimensional approach allows for more consistent and coherent meme clusters.
Implications for Artificial Reality, Augmented Reality, and Virtual Realities
The relevance of meme clustering extends beyond multimedia information systems to fields such as artificial reality, augmented reality, and virtual realities. Memes play a significant role in shaping online culture, and the ability to cluster them effectively enables the creation of immersive experiences that reflect real-world dynamics.
For example, in virtual reality environments, the clustering of memes could enhance user experiences by ensuring a coherent representation of cultural references and humor. In augmented reality applications, meme clustering could aid in the creation of contextually relevant overlays that align with the user’s surroundings. Additionally, in artificial reality simulations, understanding the clustering patterns of memes could assist in generating more natural and relatable virtual characters.
Supporting Future Research
The authors of the paper have made all their supporting code publicly available, which serves as a valuable resource for subsequent research. This availability enables researchers to build upon the proposed method and further advance the field of meme clustering. Consequently, this open-source approach can foster collaboration and accelerate the development of more robust and comprehensive clustering techniques.
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Overall, the introduction of this novel meme clustering method represents a significant advancement in the field. By considering the multi-dimensionality of memes and their cultural context, the proposed approach addresses the limitations of previous methods. Its impact expands beyond multimedia information systems to various areas, including artificial reality, augmented reality, and virtual realities.