Deep Learning-Based Hateful Meme Detection via Multimodal Feature Integration with CNN (International Journal of Engineering & Extended Technologies Research (IJEETR), 2026)
The rapid expansion of social media platforms has led to an unprecedented increase in the creation and circulation of memes, many of which contain harmful, hateful, or discriminatory content. Unlike traditional hate speech, hateful memes often combine images and text to convey implicit messages, sarcasm, or coded language that cannot be accurately detected through text-only or image-only analysis. This multimodal nature makes detection significantly more challenging, as understanding the true intent requires analyzing the relationship between visual and textual elements simultaneouslyThe rapid expansion of social media platforms has led to an unprecedented increase in the creation and circulation of memes, many of which contain harmful, hateful, or discriminatory content. Unlike traditional hate speech, hateful memes often combine images and text to convey implicit messages, sarcasm, or coded language that cannot be accurately detected through text-only or image-only analysis. This multimodal nature makes detection significantly more challenging, as understanding the true intent requires analyzing the relationship between visual and textual elements simultaneouslyThe integration of multimodal deep learning frameworks significantly enhances detection performance by aligning semantic information from both modalities. Such systems demonstrate improved robustness, better generalization to unseen meme formats, and higher accuracy in identifying implicit hate
Keywords: Hateful Meme Detection; Multimodal Learning; Deep Learning; Vision Transformer (ViT); CLIP Model; Contrastive Learning; Social Media Analysis; Hate Speech Detection; Artificial Intelligence; Content Moderation