@inproceedings{jing-etal-2025-hvguard,
    title = "{HVG}uard: Utilizing Multimodal Large Language Models for Hateful Video Detection",
    author = "Jing, Yiheng  and
      Zhang, Mingming  and
      Zhuang, Yong  and
      Guo, Jiacheng  and
      Wang, Juan  and
      Xu, Xiaoyang  and
      Yi, Wenzhe  and
      Guo, Keyan  and
      Hu, Hongxin",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.emnlp-main.456/",
    doi = "10.18653/v1/2025.emnlp-main.456",
    pages = "9004--9017",
    ISBN = "979-8-89176-332-6",
    abstract = "The rapid growth of video platforms has transformed information dissemination and led to an explosion of multimedia content. However, this widespread reach also introduces risks, as some users exploit these platforms to spread hate speech, which is often concealed through complex rhetoric, making hateful video detection a critical challenge. Existing detection methods rely heavily on unimodal analysis or simple feature fusion, struggling to capture cross-modal interactions and reason through implicit hate in sarcasm and metaphor. To address these limitations, we propose HVGuard, the first reasoning-based hateful video detection framework with multimodal large language models (MLLMs). Our approach integrates Chain-of-Thought (CoT) reasoning to enhance multimodal interaction modeling and implicit hate interpretation. Additionally, we design a Mixture-of-Experts (MoE) network for efficient multimodal fusion and final decision-making. The framework is modular and extensible, allowing flexible integration of different MLLMs and encoders. Experimental results demonstrate that HVGuard outperforms all existing advanced detection tools, achieving an improvement of 6.88{\%} to 13.13{\%} in accuracy and 9.21{\%} to 34.37{\%} in M-F1 on two public datasets covering both English and Chinese."
}