通过YouTube视频的多模态聚类表征多媒体信息环境
Characterizing Multimedia Information Environment through Multi-modal Clustering of YouTube Videos
AI总结：
提出融合音频、视频与文本信号的多模态聚类框架，基于160个南中国海YouTube视频识别主题、内容类型及再利用现象，以表征多媒体信息环境。
摘要：
本研究旨在调查多媒体（视频）中信息内容的全面表征，尤其是在YouTube上。该研究提出了一种多方法框架，通过对来自音频、视频和文本等各种模态的信号进行聚类来表征多媒体内容。该方法以南中国海相关视频作为案例研究重点，旨在增进我们对在线内容（尤其是YouTube上内容）的理解。数据集包含160个视频，研究结果基于聚类提供了对视频不同模态内内容主题和模式的洞察。文本模态分析揭示了与地缘政治国家、战略和全球安全相关的主题，而视频和音频模态分析则识别出与不同视频集合相关的独特信号模式，包括新闻分析/报道、教育内容和访谈。此外，研究结果揭示了视频聚类内的内容再利用实例，这些实例通过条形码技术和音频相似性评估得到识别。这些发现表明存在潜在的内容放大技术。总之，本研究以独特方式基于模态聚类技术增进了我们当前对多媒体内容信息的理解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
