YouTube has today become the primary news source for many users, which raises concerns about the role its recommendation algorithm can play in the spread of misinformation and political polarization. Prior work in this area has mainly analyzed how recommendations evolve based on users' watch history within the platform. Nevertheless, recommendations can also depend on off-platform browsing activity that Google collects via trackers on news websites, a factor that has not been considered so far. To fill this gap, we propose a sock-puppet-based experimental framework that automatically interacts with news media articles and then collects YouTube recommendations to measure how cross-site tracking affects the political and misinformation content users see. Moreover, by running our audits in both tracking-permissive and tracking-restrictive browser environments, we assess whether common privacy-focused browsers can protect users from tracking-driven political and misinformation bubbles on YouTube.

翻译：如今，YouTube已成为许多用户的主要新闻来源，这引发了对其推荐算法在虚假信息传播和政治极化中可能扮演的角色的担忧。该领域先前的研究主要分析了推荐如何根据用户在平台内的观看历史演变。然而，推荐也可能依赖于Google通过新闻网站上的跟踪器收集的站外浏览活动，这一因素至今未被纳入考量。为填补这一空白，我们提出一个基于傀儡账户的实验框架，该框架自动与新闻媒体文章交互，随后收集YouTube推荐，以衡量跨站跟踪如何影响用户看到的政治与虚假信息内容。此外，通过在允许跟踪和限制跟踪的浏览器环境中运行审计，我们评估了常见的隐私导向浏览器能否保护用户免受YouTube上由跟踪驱动的政治与虚假信息过滤气泡的影响。