  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.
