arXiv:2505.12051v1 Announce Type: new
Abstract: The rapid rise of video content on platforms such as TikTok and YouTube has transformed information dissemination, but it has also facilitated the spread of harmful content, particularly hate videos. Despite significant efforts to combat hate speech, detecting these videos remains challenging due to their often implicit nature. Current detection methods primarily rely on unimodal approaches, which inadequately capture the complementary features across different modalities. While multimodal techniques offer a broader perspective, many fail to effectively integrate temporal dynamics and modality-wise interactions essential for identifying nuanced hate content. In this paper, we present CMFusion, an enhanced multimodal hate video detection model utilizing a novel Channel-wise and Modality-wise Fusion Mechanism. CMFusion first extracts features from text, audio, and video modalities using pre-trained models and then incorporates a temporal cross-attention mechanism to capture dependencies between video and audio streams. The learned features are then processed by channel-wise and modality-wise fusion modules to obtain informative representations of videos. Our extensive experiments on a real-world dataset demonstrate that CMFusion significantly outperforms five widely used baselines in terms of accuracy, precision, recall, and F1 score. Comprehensive ablation studies and parameter analyses further validate our design choices, highlighting the model’s effectiveness in detecting hate videos. The source codes will be made publicly available at https://github.com/EvelynZ10/cmfusion.
Expert Commentary: The Rise of Multimodal Approaches in Hate Video Detection
The proliferation of video content on social media platforms has brought about both positive and negative consequences. While it has democratized information dissemination and fostered creativity, it has also facilitated the spread of harmful content, such as hate videos. These videos often contain implicit messages that can be challenging to detect using traditional methods.
Current hate video detection approaches predominantly rely on unimodal techniques, which may not fully capture the complexity of multimedia content. Multimodal methods, on the other hand, leverage information from multiple modalities, such as text, audio, and video, to provide a more comprehensive understanding of the content. However, integrating temporal dynamics and modality-wise interactions in these approaches remains a challenge.
The CMFusion model introduced in this paper takes a step towards addressing this issue by utilizing a Channel-wise and Modality-wise Fusion Mechanism. By extracting features from different modalities and incorporating a temporal cross-attention mechanism, CMFusion aims to capture the nuanced relationships between video and audio streams. The model then processes these features using fusion modules to generate informative representations of hate videos.
Notably, the effectiveness of CMFusion is demonstrated through extensive experiments on a real-world dataset, where it outperforms five popular baselines in terms of accuracy, precision, recall, and F1 score. Ablation studies and parameter analyses further validate the design choices of the model, emphasizing its robustness in hate video detection.
From a multidisciplinary perspective, the development of CMFusion touches upon various fields, including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. As hate videos can manifest in different forms across these modalities, a holistic approach that combines insights from diverse disciplines is essential in combating harmful content online.
In conclusion, the integration of multimodal techniques, like CMFusion, represents a promising direction in addressing the challenges of hate video detection. By leveraging the complementary features of different modalities and incorporating advanced fusion mechanisms, researchers can enhance the accuracy and effectiveness of automated content moderation systems in the digital age.