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, hate speech research has recently gained a lot of traction. However, most of the work has primarily focused on text media with relatively little work on images and even lesser on videos. Thus, early stage automated video moderation techniques are needed to handle the videos that are being uploaded to keep the platform safe and healthy. With a view to detect and remove hateful content from the video sharing platforms, our work focuses on hate video detection using multi-modalities. To this end, we curate ~43 hours of videos from BitChute and manually annotate them as hate or non-hate, along with the frame spans which could explain the labelling decision. To collect the relevant videos we harnessed search keywords from hate lexicons. We observe various cues in images and audio of hateful videos. Further, we build deep learning multi-modal models to classify the hate videos and observe that using all the modalities 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-modal model in terms of macro F1 score. In summary, our work takes the first step toward understanding and modeling hateful videos on video hosting platforms such as BitChute.

翻译：摘要：仇恨言论已成为现代社会最严峻的问题之一，其对线上及线下世界均产生深远影响。正因如此，仇恨言论研究近年来获得了广泛关注。然而，现有工作主要聚焦于文本媒介，涉及图像的研究相对较少，而针对视频的研究则更为稀缺。因此，亟需开发早期自动化视频审核技术，以处理不断上传的视频内容，保障平台的安全与健康环境。为检测并移除视频分享平台中的仇恨性内容，本研究聚焦于利用多模态特征进行仇恨视频检测。为此，我们从BitChute平台收集了约43小时的视频数据，并人工标注其是否含有仇恨信息，同时标注可解释分类决策的关键帧区间。我们通过仇恨词汇库提取搜索关键词，以获取相关视频样本。在分析仇恨视频的图像与音频特征时，我们发现了多种判别性线索。进一步地，我们构建了深度学习多模态模型以对仇恨视频进行分类，实验表明：相较于最优单模态模型（以宏F1分数为指标），融合视频所有模态可将整体仇恨言论检测性能提升约5.7%（准确率=0.798，宏F1分数=0.790）。综上，本研究迈出了理解并建模BitChute等视频托管平台中仇恨性视频的第一步。