Abstract
The rapid proliferation of online multimedia content has intensified the spread of hate speech, presenting critical societal and regulatory challenges. While recent work has advanced multimodal hateful video detection, most approaches rely on coarse, video-level annotations that overlook the temporal granularity of hateful content. This introduces substantial label noise, as videos annotated as hateful often contain long non-hateful segments. In this paper, we investigate the impact of such label ambiguity through a fine-grained approach. Specifically, we trim hateful videos from the HateMM and MultiHateClip English datasets using annotated timestamps to isolate explicitly hateful segments. We then conduct an exploratory analysis of these trimmed segments to examine the distribution and characteristics of both hateful and non-hateful content. This analysis highlights the degree of semantic overlap and the confusion introduced by coarse, video-level annotations. Finally, controlled experiments demonstrated that time-stamp noise fundamentally alters model decision boundaries and weakens classification confidence, highlighting the inherent context dependency and temporal continuity of hate speech expression. Our findings provide new insights into the temporal dynamics of multimodal hateful videos and highlight the need for temporally aware models and benchmarks for improved robustness and interpretability. Code and data are available at this https URL.
Abstract (translated)
在线多媒体内容的迅速增长加剧了仇恨言论的传播，带来了重要的社会和监管挑战。尽管最近的工作在多模态仇恨视频检测方面有所进展，但大多数方法依赖于粗略的、以视频为单位的标注，这忽视了仇恨内容的时间粒度。这种做法引入了大量的标签噪音，因为被标记为仇恨的内容通常包含长时间非仇恨的部分。在这篇论文中，我们通过细粒度的方法研究了此类标签模糊性的影响。 具体来说，我们将HateMM和MultiHateClip英文数据集中标注有时间戳的仇恨视频进行裁剪，以分离出明确的仇恨片段。然后，我们对这些裁剪后的片段进行了探索性分析，考察了仇恨与非仇恨内容的分布和特征。这一分析突显了语义重叠的程度以及粗略、基于视频级别的标签带来的混淆。 最后，在控制实验中证明时间戳噪音从根本上改变了模型决策边界，并削弱了分类信心，强调了仇恨言论表达的内在上下文依赖性和时间连续性。我们的研究结果为多模态仇恨视频的时间动态提供了新的见解，并突显了开发具有时间意识的模型和基准的重要性，以提高其鲁棒性和可解释性。 代码和数据可在以下链接获取：[此URL]（请将"this https URL"替换为实际提供的网址）。
URL
https://arxiv.org/abs/2508.04900
