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Hate speech has become one of the most significant issues in modern society,having implications in both the online and the offline world. Due to this, hatespeech research has recently gained a lot of traction. However, most of thework has primarily focused on text media with relatively little work on imagesand even lesser on videos. Thus, early stage automated video moderationtechniques are needed to handle the videos that are being uploaded to keep theplatform safe and healthy. With a view to detect and remove hateful contentfrom the video sharing platforms, our work focuses on hate video detectionusing multi-modalities. To this end, we curate ~43 hours of videos fromBitChute and manually annotate them as hate or non-hate, along with the framespans which could explain the labelling decision. To collect the relevantvideos we harnessed search keywords from hate lexicons. We observe various cuesin images and audio of hateful videos. Further, we build deep learningmulti-modal models to classify the hate videos and observe that using all themodalities of the videos improves the overall hate speech detection performance(accuracy=0.798, macro F1-score=0.790) by ~5.7% compared to the best uni-modalmodel in terms of macro F1 score. In summary, our work takes the first steptoward understanding and modeling hateful videos on video hosting platformssuch as BitChute.

HateMM: A Multi-Modal Dataset for Hate Video Classification