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Video moderation, which refers to remove deviant or explicit content frome-commerce livestreams, has become prevalent owing to social and engagingfeatures. However, this task is tedious and time consuming due to thedifficulties associated with watching and reviewing multimodal video content,including video frames and audio clips. To ensure effective video moderation,we propose VideoModerator, a risk-aware framework that seamlessly integrateshuman knowledge with machine insights. This framework incorporates a set ofadvanced machine learning models to extract the risk-aware features frommultimodal video content and discover potentially deviant videos. Moreover,this framework introduces an interactive visualization interface with threeviews, namely, a video view, a frame view, and an audio view. In the videoview, we adopt a segmented timeline and highlight high-risk periods that maycontain deviant information. In the frame view, we present a novel visualsummarization method that combines risk-aware features and video context toenable quick video navigation. In the audio view, we employ a storyline-baseddesign to provide a multi-faceted overview which can be used to explore audiocontent. Furthermore, we report the usage of VideoModerator through a casescenario and conduct experiments and a controlled user study to validate itseffectiveness.

VideoModerator: A Risk-aware Framework for Multimodal Video Moderation in E-Commerce