Comprehensively Auditing the TikTok Mobile App

TikTok has become a dominant force in the social media landscape of the United States, and has spawned other social media sites emulating their algorithmically-driven short form content recommendation platform (e.g. Youtube Shorts and Instagram Reels). The short-form vertical content is designed to be consumed on mobile phones, but existing audits have predominantly, and to a limited degree, investigated TikTok using the web application. Additionally, there are no advertisements on the web version of TikTok, and as such the advertising ecosystem of the platform has thusfar largely gone unstudied. In this work we propose a technique for auditing TikTok's recommendation algorithm through interfacing with emulators and intercepting network traffic. In this way we are able to measure the personalization that comes from user-specified demographics such as gender and age and better understand how ads are delivered to these groups. Future work will investigate personalization from user interaction such as liking posts and following creators based on their interest, and will study the role that algorithmic personalization plays in ad targeting.