arXiv:2512.02743v2 Announce Type: replace-cross Abstract: Hate speech in online videos is posing an increasingly serious threat to digital platforms, especially as video content becomes increasingly multimodal and context-dependent. Existing methods often struggle to effectively fuse the complex semantic relationships between modalities and lack the ability to understand nuanced hateful content. To address these issues, we propose an innovative Reasoning-Aware Multimodal Fusion (RAMF) framework. To tackle the first challenge, we design Local-Global Context Fusion (LGCF) to capture both local s
The proliferation of video content online, combined with advancements in AI's ability to process multimodal data, makes nuanced hate speech detection a critical and solvable problem now.
Detecting complex hateful content in videos is crucial for maintaining safe digital platforms and mitigating the societal impact of online hate speech, affecting platform integrity and user experience.
This research introduces a more sophisticated method for understanding multimodal, context-dependent hate speech in videos, potentially leading to more effective content moderation tools.
- · Digital platforms
- · AI ethics researchers
- · Content moderation technology providers
- · Social media users
- · Perpetrators of online hate speech
Improved detection of hateful video content will lead to more effective moderation efforts by digital platforms.
A reduction in visible hate speech may foster safer online environments and potentially influence user engagement patterns.
More sophisticated detection could force those creating hateful content to evolve their tactics, leading to an ongoing technological arms race in content moderation.
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Read at arXiv cs.AI