Detecting Offensive Memes with Social Biases in Singapore Context Using Multimodal Large Language Models

Traditional online content moderation systems struggle to classify modern multimodal means of communication, such as memes, a highly nuanced and information-dense medium. This task is especially hard in a culturally diverse society like Singapore, where low-resource languages are used and extensive knowledge on local context is needed to interpret online content. We curate a large collection of 112K memes labeled by GPT-4V for fine-tuning a VLM to classify offensive memes in Singapore context. We show the effectiveness of fine-tuned VLMs on our dataset, and propose a pipeline containing OCR, translation and a 7-billion parameter-class VLM. Our solutions reach 80.62% accuracy and 0.8192 AUROC on a held-out test set, and can greatly aid human in moderating online contents. The dataset, code, and model weights have been open-sourced atthis https URL.

@article{yuxuan2025_2502.18101,
  title={ Detecting Offensive Memes with Social Biases in Singapore Context Using Multimodal Large Language Models },
  author={ Cao Yuxuan and Wu Jiayang and Alistair Cheong Liang Chuen and Bryan Shan Guanrong and Theodore Lee Chong Jen and Sherman Chann Zhi Shen },
  journal={arXiv preprint arXiv:2502.18101},
  year={ 2025 }
}