Hateful videos have become prevalent on online platforms, highlighting an urgent need for effective detection. However, existing studies primarily focus on binary classification and fail to provide contextual rationales that reveal the implicit meanings behind these judgments, significantly undermining model explainability. To fill this gap, we aim to achieve explainable hateful video detection, enabling models to provide contextual rationales that integrate relevant evidence and logical reasoning alongside decisions. This approach can comprehensively enhance the understanding of video content and the explainability of the decision-making process. We first introduce two datasets, Ex-HateMM and Ex-ImpliHateVid, for explainable hateful video detection. Each dataset provides fine-grained annotations of multimodal harmful elements, along with contextual rationales. We then propose an Information Augmentation and Reasoning Enhancement (IARE) framework designed for explainable detection. The framework employs an information augmentation phase that leverages the multimodal chain-of-thought to integrate harmful elements, thereby enriching rationale evidence. Additionally, IARE incorporates a reasoning enhancement phase, in which Direct Preference Optimization guides the model toward correct reasoning paths and away from incorrect ones, thereby improving the logical coherence of its justifications. We conduct extensive experiments on the two datasets, comparing multiple baselines with our proposed IARE framework. The results demonstrate that IARE achieves state-of-the-art performance while also generating accurate rationales.

翻译：仇恨视频在网络平台上日益泛滥，凸显了有效检测的迫切需求。然而，现有研究主要聚焦于二元分类，未能提供揭示这些判断背后隐含含义的情境化推理依据，这严重削弱了模型的可解释性。为填补这一空白，我们旨在实现可解释的仇恨视频检测，使模型能够在决策的同时提供整合相关证据与逻辑推理的情境化推理依据。该方法能够全面增强对视频内容的理解以及决策过程的可解释性。我们首先引入了两个用于可解释仇恨视频检测的数据集——Ex-HateMM和Ex-ImpliHateVid。每个数据集都提供了多模态有害元素的细粒度标注以及情境化推理依据。接着，我们提出了一种面向可解释检测的信息增强与推理优化框架（IARE）。该框架采用信息增强阶段，利用多模态思维链整合有害元素，从而丰富推理证据。此外，IARE还集成了推理优化阶段，在此阶段中，直接偏好优化引导模型走向正确的推理路径并远离错误路径，从而提升其论证逻辑的一致性。我们在两个数据集上进行了大量实验，将多个基线方法与所提出的IARE框架进行对比。结果表明，IARE在取得最先进性能的同时，还能生成准确的推理依据。