了解信息使用YouTubeTracker的操作

Thomas Marcoux, Nitin Agarwal, A. Obadimu, Muhammad Nihal Hussain, K. Galeano, Samer Al-khateeb
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引用次数: 4

摘要

YouTube是世界上第二受欢迎的网站。每分钟有超过300个小时的视频被上传,每天有50亿个视频被观看——几乎全世界每个人都有一个视频。因为视频可以传递复杂的信息,比基于文本的平台更有效地抓住观众的注意力,它已经成为数字大众传播时代最重要的平台之一。这使得对YouTube内容和用户行为的分析不仅对信息科学家,而且对传播研究人员、记者、社会学家等许多人来说都是无价的。现在有很多YouTube分析工具,但没有一个能对用户行为或网络提供深入的定性和定量分析。朝着这个方向,我们推出了YouTubeTracker——一个旨在收集YouTube数据并获得内容和用户见解的工具。这一工具有助于确定主要行为者、网络和影响范围、新出现的流行趋势以及用户意见。这种分析也可以用来理解用户粘性和社交网络。这可以帮助揭示引起算法操纵的可疑和无机行为(例如,trolling, bot)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding Information Operations using YouTubeTracker
YouTube is the second most popular website in the world. Over 300 hours worth of videos are uploaded every minute and 5 billion videos are watched every day - almost one video per person worldwide. Because videos can deliver a complex message in a way that captures the audience's attention more effectively than text-based platforms, it has become one of the most relevant platforms in the age of digital mass communication. This makes the analysis of YouTube content and user behavior invaluable not only to information scientists but also communication researchers, journalists, sociologists, and many more. There exists a number of YouTube analysis tools but none of them provide an in-depth qualitative and quantitative insights into user behavior or networks. Towards that direction, we introduce YouTubeTracker - a tool designed to gather YouTube data and gain insights on content and users. This tool can help identify leading actors, networks and spheres of influence, emerging popular trends, as well as user opinion. This analysis can also be used to understand user engagement and social networks. This can help reveal suspicious and inorganic behaviors (e.g., trolling, botting) causing algorithmic manipulations.
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