TikTok currently is the fastest growing social media platform with over 1 billion active monthly users of which the majority is from generation Z. Arguably, its most important success driver is its recommendation system. Despite the importance of TikTok's algorithm to the platform's success and content distribution, little work has been done on the empirical analysis of the algorithm. Our work lays the foundation to fill this research gap. Using a sock-puppet audit methodology with a custom algorithm developed by us, we tested and analysed the effect of the language and location used to access TikTok, follow- and like-feature, as well as how the recommended content changes as a user watches certain posts longer than others. We provide evidence that all the tested factors influence the content recommended to TikTok users. Further, we identified that the follow-feature has the strongest influence, followed by the like-feature and video view rate. We also discuss the implications of our findings in the context of the formation of filter bubbles on TikTok and the proliferation of problematic content.

翻译：TikTok目前是增长最快的社交媒体平台,每月有10亿以上的活跃用户,其中大部分来自Z一代。 可以说,其最重要的成功驱动力是其推荐系统。尽管TikTok的算法对于平台的成功和内容发布十分重要,但对算法的经验分析却没有做多少工作。我们的工作为填补这一研究差距奠定了基础。我们使用一种由我们开发的定制算法来测试并分析了用于访问TikTok、跟踪和类似功能的语言和位置的影响,以及推荐的内容在用户观看某些职位的时间比其他职位更长的情况下是如何改变的。我们提供了证据,证明所有经过测试的因素都影响着向TikTok用户推荐的内容。此外,我们发现,后续功能影响最大,其次是类似功能和视频浏览率。我们还在形成TikTok的过滤泡泡和问题内容扩散的背景下讨论了我们发现的结果的影响。