Explainable Hate Meme Detection Using Multimodal Learning

This book presents an advanced approach to detecting hateful content in internet memes using multimodal learning and explainable artificial intelligence (XAI). Memes combine images and text, making their interpretation complex, especially when harmful intent is conveyed indirectly through sarcasm, symbolism, or contextual cues. Traditional text-based systems often fail to capture such relationships. To address this challenge, the proposed Explainable Hate Meme Detection System (EHMDS) integrates deep learning models for both visual and textual feature extraction, followed by an attention-based fusion mechanism to understand cross-modal interactions. The system further incorporates explainability techniques such as Grad-CAM and attention visualization to provide transparent and human-understandable predictions. Evaluated on benchmark datasets, the model demonstrates improved accuracy and interpretability compared to existing approaches. This work contributes to the development of reliable AI systems for content moderation and safer digital platforms.