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PolyMeme: Fine-Grained Internet Meme Sensing
Vasileios Arailopoulos1, Christos Koutlis2, Symeon Papadopoulos2
1School of Electrical & Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
Sensors (Basel, Switzerland)
|September 14, 2024
Summary
This study introduces PolyMeme, a diverse dataset for automatically detecting internet memes. The new dataset and deep learning models achieve high accuracy in identifying meme content, aiding in tracking harmful online information.
Area of Science:
- Computer Science
- Social Media Analysis
- Artificial Intelligence
Background:
- Internet memes are a multimodal digital content format popular on social media.
- Automatic meme detection is crucial for tracking trends and harmful content spread.
- Existing datasets lack diversity in meme formats, styles, and content.
Purpose of the Study:
- To introduce the PolyMeme dataset, a diverse collection of approximately 27,000 memes across four categories.
- To address the limitations of existing datasets in capturing meme variety.
- To develop and evaluate deep learning models for accurate meme detection.
Main Methods:
- Collected approximately 27,000 memes from Reddit, categorizing them.
- Manually labeled a portion of the dataset for training and validation.
- Trained deep learning networks (ResNet, ViT) using the PolyMeme dataset and other image datasets for meme detection.
Main Results:
- Deep learning models trained on PolyMeme achieved an estimated error rate of 7.35% for classification.
- Meme detection models demonstrated high accuracy, reaching 98% on the test set.
- The inclusion of regular images with text did not significantly improve meme detection performance.
Conclusions:
- The PolyMeme dataset enhances meme detection capabilities by accounting for diverse meme formats.
- Accurate automatic meme detection is feasible with advanced deep learning techniques and comprehensive datasets.
- This work contributes to better understanding and mitigating the spread of online misinformation and harmful content through memes.
