User dynamics and sensitivity analysis of meta-data on the YouTube social network

YouTube 社交网络元数据的用户动态和敏感性分析

• 批准号: 478161-2015
• 负责人: Krishnamurthy, Vikram
• 金额: $ 1.6万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=579333
• 关键词: User; dynamics; sensitivity; analysis; meta

There is significant motivation to determine what descriptors of a YouTube video or channels maximize the the number of views. The goal of this research project is to apply the principle of revealed preferences together with statistical analysis and machine learning techniques to quantify how metadata (titles, descriptions, keywords, and thumbnails) associated with YouTube videos affects the static and dynamic response of YouTube users. A major contribution of this research is a non-parametric feasibility test to detect if YouTube users are utility maximizers in their response to meta-data. The feasibility test only requires the observable meta-data and responses of users. If uses are utility maximizer then their associated utility functions, which encode their preferences, can be constructed without prior assumptions on the utility. A non-parametric learning algorithm can then be used to predict the response of users to variations in meta-level data. Having estimated the utility function of YouTube users, we then use statistical and machine learning techniques to study how meta-level data effects the popularity dynamics of YouTube videos. We apply the feasibility test and machine learning techniques to a decade of YouTube data collected from BroadbandTV. The results provide valuable insight into which meta-level data to adjust to increase the popularity of videos.

确定YouTube视频或频道的哪些描述符最大化了 浏览次数。这个研究项目的目标是一起应用显示偏好的原则 利用统计分析和机器学习技术来量化元数据（标题，描述， 关键字和缩略图）影响YouTube视频的静态和动态响应， YouTube用户。这项研究的一个主要贡献是一个非参数可行性测试，以检测YouTube是否 用户在对元数据的响应中是效用最大化者。可行性测试只需要可观察的 元数据和用户的反应。如果用户是效用最大化者，则其相关的效用函数， 编码他们的偏好，可以构建没有事先假设的效用。非参数 然后，可以使用学习算法来预测用户对元级数据中的变化的响应。具有 估计YouTube用户的效用函数，然后使用统计和机器学习技术， 研究元数据如何影响YouTube视频的流行动态。我们进行可行性测试， 从BroadbandTV收集的十年YouTube数据中使用机器学习技术。结果提供 有价值的洞察力，以调整哪些元级数据，以增加视频的受欢迎程度。