Towards resource inequities in catching the “dark side” of social media: A hateful meme classification framework for low-resource scenarios
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2024-01-03
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7215
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The increasing prevalence of social media platforms has led to the emergence of multimodal information such as memes. Hateful memes poses a risk by perpetuating discrimination, reinforcing stereotypes, and causing online harassment, thereby marginalising certain groups and impeding efforts towards inclusivity and social justice. Detecting hateful memes is crucial for creating a safe and equitable online environment. However, existing research heavily relies on complex and large deep learning models, requiring substantial computational resources for training. This creates a barrier for under-resourced researchers and small companies, limiting their participation in hateful information detection and exacerbating inequalities in the field of artificial intelligence. This paper attempts to tackle the problem by proposing a low-resource- oriented framework of hateful meme classification to address limitations in training data, computing power, and modality integration. Our approach achieves faster performance with reduced computational requirements, while maintaining a 94.7% accuracy comparable to the existing highest-scoring model.
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The Bright and Dark Side of Social Media in the Marginalized Contexts, data augmentation, deep learning, hateful meme classification, knowledge distillation, low- resource nlp
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10 pages
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Proceedings of the 57th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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