Meme-Integrated Deep Learning: A Multimodal Classification Fusion Framework to Fuse Meme Culture into Deep Learning

DOI

10.2991/978-94-6463-300-9_14How to use a DOI?

Keywords

Multimodal Classification; Meme-Integrated; Deep Learning

Abstract

Memes are an important medium of expression in online communication, yet traditional methods such as collaborative filtering (CF) have limitations in processing multimodal data, especially when analyzing memes has limitations in processing large-scale datasets and are sensitive to data noise and sparsity. In addition to CF, support vector machine (SVM) is a standard classification algorithm. Still, both methods are susceptible to data noise and sparsity, which can decrease classifier performance. We propose a Meme-Integrated Deep Learning (MIDL) approach that leverages deep learning techniques to classify and analyze memes. The MIDL framework integrates visual and textual modalities of memes, providing a powerful tool for understanding meme culture. Our approach achieves state-of-the-art performance on a meme classification task, overcoming the limitations of traditional methods like CF and SVM. Combining the advantages of deep learning and meme culture, our approach provides new insights into how we communicate and interact online and contributes to developing more intelligent and effective recommendation systems. The proposed MIDL framework has the potential to advance research in online culture and social media analysis by providing a more accurate and efficient way to process multimodal data.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xuxiang Deng
AU  - Yifan Liu
AU  - Qihao Yan
PY  - 2023
DA  - 2023/11/27
TI  - Meme-Integrated Deep Learning: A Multimodal Classification Fusion Framework to Fuse Meme Culture into Deep Learning
BT  - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)
PB  - Atlantis Press
SP  - 130
EP  - 145
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-300-9_14
DO  - 10.2991/978-94-6463-300-9_14
ID  - Deng2023
ER  -