Numerous discussions have advocated the presence of a so called rabbit-hole (RH) phenomenon on social media, interested in advanced personalization to their users. This phenomenon is loosely understood as a collapse of mainstream recommendations, in favor of ultra personalized ones that lock users into narrow and specialized feeds. Yet quantitative studies are often ignoring personalization, are of limited scale, and rely on manual tagging to track this collapse. This precludes a precise understanding of the phenomenon based on reproducible observations, and thus the continuous audits of platforms. In this paper, we first tackle the scale issue by proposing a user-sided bot-centric approach that enables large scale data collection, through autoplay walks on recommendations. We then propose a simple theory that explains the appearance of these RHs. While this theory is a simplifying viewpoint on a complex and planet-wide phenomenon, it carries multiple advantages: it can be analytically modeled, and provides a general yet rigorous definition of RHs. We define them as an interplay between i) user interaction with personalization and ii) the attraction strength of certain video categories, which cause users to quickly step apart of mainstream recommendations made to fresh user profiles. We illustrate these concepts by highlighting some RHs found after collecting more than 16 million personalized recommendations on YouTube. A final validation step compares our automatically-identified RHs against manually-identified RHs from a previous research work. Together, those results pave the way for large scale and automated audits of the RH effect in recommendation systems.

翻译：众多讨论指出社交媒体中存在所谓的“兔子洞”（Rabbit-Hole，RH）现象，该现象与平台对用户的深度个性化定制密切相关。这一现象被宽泛地理解为：主流推荐机制崩溃，取而代之的是过度个性化的推荐，将用户锁定在狭窄且专业化的信息流中。然而，现有定量研究往往忽视个性化因素，规模有限，且依赖人工标注来追踪这种崩溃。这导致无法基于可复现的观测精准理解该现象，从而无法对平台进行持续审计。本文首先通过提出一种用户端、以机器人为核心的方法来解决规模问题——该方法利用自动播放模式遍历推荐系统，实现大规模数据收集。随后我们提出一个简洁理论来解释这些“兔子洞”的出现。尽管该理论是对这一复杂全球性现象的简化视角，但它具有多重优势：可进行分析建模，并为“兔子洞”提供了一般且严谨的定义。我们将其定义为以下两者的相互作用：i）用户与个性化机制的交互，以及ii）特定视频类别的吸引力强度，这种相互作用导致用户迅速偏离为新用户画像提供的推荐主流。通过收集YouTube上超过1600万条个性化推荐数据，我们验证了这些概念，并揭示了部分发现的“兔子洞”。最终验证步骤将我们自动识别出的“兔子洞”与先前研究工作中手动标注的“兔子洞”进行对比。这些结果共同为大规模自动化审计推荐系统中的“兔子洞”效应铺平了道路。