ViSeHate: A Large-Scale Benchmark Dataset for Hate Detection and Temporal Localization in Multimodal Videos

The rapid rise of short-video platforms has accelerated the spread of multimodal hate speech characterized by covert and semantically complex cues. Existing datasets struggle to support real-world content moderation due to limited scale, single-platform bias, and the absence of fine-grained temporal localization annotations. To address these limitations, we introduce ViSeHate, a large-scale and cross-platform benchmark dataset designed for both video-level hate detection and frame-level localization. The dataset comprises ViSeHate-Det (10,000 videos across four platforms and six protected attributes) and ViSeHate-Loc (1,200 videos with precise frame-level boundaries). 

This upload of the ViSeHate-Det dataset contains labeled videos from Rumble, Dailymotion, YouTube platform.

短视频平台的迅猛崛起，加速了以隐蔽性强、语义复杂为特征的多模态仇恨言论的传播。现有数据集因规模有限、存在单平台偏差且缺乏细粒度时序定位标注，难以支撑真实场景下的内容审核工作。为解决上述局限性，我们推出ViSeHate——一款专为视频级仇恨检测与帧级定位任务设计的大规模跨平台基准数据集。该数据集包含两大子数据集：ViSeHate-Det（覆盖四大平台、六大受保护属性的10000条视频）与ViSeHate-Loc（具备精确帧级边界标注的1200条视频）。
本次上传的ViSeHate-Det数据集包含来自Rumble、Dailymotion、YouTube平台的标注视频。