Abstract
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.
Abstract (translated)
恶言已经成为现代社会中最重要的问题之一，它在在线和离线世界中都具有重要意义。因此，恶言研究最近取得了很多进展。然而，大部分研究主要关注文本媒体，对于图像和视频的研究相对较少。因此，需要使用早期阶段的自动化视频编辑技术来处理正在上传的视频，以保持平台安全和健康。为了检测和删除视频分享平台上的仇恨内容，我们的研究重点是使用多模态方法检测恶言视频。为此，我们整理BitChute上的 ~43小时视频，并手动标注它们是否是恶言或非恶言，并考虑每个帧的跨度以解释标签的决定。为了收集相关视频，我们从仇恨词汇库中检索关键词。我们观察仇恨视频图像和音频中的各种线索。进一步，我们构建深度学习多模态模型来分类恶言视频，并观察到使用所有视频模态可以提高整体恶言检测表现(准确率=0.798，宏观F1得分=0.790)相比宏观F1得分最佳的单模态模型高出约5.7%。总之，我们的研究迈出了理解并建模像BitChute这样的视频托管平台中的仇恨视频的第一步。
URL
https://arxiv.org/abs/2305.03915
