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.