Meme-ingful Analysis: Enhanced Understanding of Cyberbullying in Memes Through Multimodal Explanations
This addresses the need for interpretable models in cyberbullying detection for online platforms, though it is incremental as it builds on existing multimodal detection methods by adding explainability.
The paper tackles the problem of explaining why memes are cyberbullying by introducing MultiBully-Ex, the first benchmark dataset for multimodal explanations from code-mixed cyberbullying memes, and proposes a CLIP-based approach that improves performance in generating textual justifications and identifying visual evidence with reliable gains.
Internet memes have gained significant influence in communicating political, psychological, and sociocultural ideas. While memes are often humorous, there has been a rise in the use of memes for trolling and cyberbullying. Although a wide variety of effective deep learning-based models have been developed for detecting offensive multimodal memes, only a few works have been done on explainability aspect. Recent laws like "right to explanations" of General Data Protection Regulation, have spurred research in developing interpretable models rather than only focusing on performance. Motivated by this, we introduce {\em MultiBully-Ex}, the first benchmark dataset for multimodal explanation from code-mixed cyberbullying memes. Here, both visual and textual modalities are highlighted to explain why a given meme is cyberbullying. A Contrastive Language-Image Pretraining (CLIP) projection-based multimodal shared-private multitask approach has been proposed for visual and textual explanation of a meme. Experimental results demonstrate that training with multimodal explanations improves performance in generating textual justifications and more accurately identifying the visual evidence supporting a decision with reliable performance improvements.