This study aims to investigate the comprehensive characterization of information content in multimedia (videos), particularly on YouTube. The research presents a multi-method framework for characterizing multimedia content by clustering signals from various modalities, such as audio, video, and text. With a focus on South China Sea videos as a case study, this approach aims to enhance our understanding of online content, especially on YouTube. The dataset includes 160 videos, and our findings offer insights into content themes and patterns within different modalities of a video based on clusters. Text modality analysis revealed topical themes related to geopolitical countries, strategies, and global security, while video and audio modality analysis identified distinct patterns of signals related to diverse sets of videos, including news analysis/reporting, educational content, and interviews. Furthermore, our findings uncover instances of content repurposing within video clusters, which were identified using the barcode technique and audio similarity assessments. These findings indicate potential content amplification techniques. In conclusion, this study uniquely enhances our current understanding of multimedia content information based on modality clustering techniques.

翻译：本研究旨在探究多媒体（视频）信息内容的全面表征方法，特别针对YouTube平台。研究提出了一种多方法框架，通过聚类来自音频、视频和文本等多模态信号来表征多媒体内容。以南海相关视频为案例研究，该方法旨在深化我们对在线内容（尤其是YouTube平台）的理解。数据集包含160个视频，研究结果基于聚类分析揭示了视频不同模态中的内容主题与模式。文本模态分析识别出涉及地缘政治国家、战略及全球安全的主题议题，而视频与音频模态分析则发现了与新闻分析/报道、教育内容和访谈等多元视频类别相关的独特信号模式。此外，研究通过条形码技术与音频相似性评估，发现了视频聚类中存在内容复用现象。这些发现揭示了潜在的内容放大技术。结论指出，本研究通过模态聚类技术，为多媒体内容信息的理解提供了独特视角。