Abstract
In an era of rapidly evolving internet technology, the surge in multimodal content, including videos, has expanded the horizons of online communication. However, the detection of toxic content in this diverse landscape, particularly in low-resource code-mixed languages, remains a critical challenge. While substantial research has addressed toxic content detection in textual data, the realm of video content, especially in non-English languages, has been relatively underexplored. This paper addresses this research gap by introducing a benchmark dataset, the first of its kind, consisting of 931 videos with 4021 code-mixed Hindi-English utterances collected from YouTube. Each utterance within this dataset has been meticulously annotated for toxicity, severity, and sentiment labels. We have developed an advanced Multimodal Multitask framework built for Toxicity detection in Video Content by leveraging Large Language Models (LLMs), crafted for the primary objective along with the additional tasks of conducting sentiment and severity analysis. ToxVidLLM incorporates three key modules the Encoder module, Cross-Modal Synchronization module, and Multitask module crafting a generic multimodal LLM customized for intricate video classification tasks. Our experiments reveal that incorporating multiple modalities from the videos substantially enhances the performance of toxic content detection by achieving an Accuracy and Weighted F1 score of 94.29% and 94.35%, respectively.
Abstract (translated)
在快速发展的互联网技术时代，多模态内容的激增已经扩展了互联网沟通的视野。然而，在多样性较高的场景中检测有毒内容，特别是低资源代码混合语言中的内容，仍然是一个关键的挑战。尽管在文本数据中的有毒内容检测方面已经进行了大量研究，但在视频内容领域，尤其是在非英语语言中，这个领域仍然相对缺乏研究。本文通过引入一个基准数据集来填补这个研究空白，这个数据集包括从YouTube上收集的931个视频，每个视频都有4021个印度-英语混合代码的语料。这个数据集中的每个语料都已详细注释为毒性、严重性和情感标签。我们利用大型语言模型（LLMs）构建了用于视频内容中有毒内容检测的先进多模态多任务框架，并专门为情感和严重性分析增加了任务。ToxVidLLM包括三个关键模块：编码器模块、跨模态同步模块和多任务模块，为复杂的视频分类任务创建了一个通用的多模态LLM。我们的实验结果表明，通过引入视频中的多个模态，可以极大地提高有毒内容检测的性能，达到94.29%的准确度和94.35%的加权F1分数。
URL
https://arxiv.org/abs/2405.20628
