More Than Sum of Its Parts: Deciphering Intent Shifts in Multimodal Hate Speech Detection

ArXi:2603.21298v1 Announce Type: cross Combating hate speech on social media is critical for securing cyberspace, yet relies heavily on the efficacy of automated detection systems. As content formats evolve, hate speech is transitioning from solely plain text to complex multimodal expressions, making implicit attacks harder to spot. Current systems, however, often falter on these subtle cases, as they struggle with multimodal content where the emergent meaning transcends the aggregation of individual modalities.