When 'For You' Isn't For You: Measuring User Agency in TikTok's Algorithmic Feed

TL;DR

This paper investigates user control over TikTok's algorithmic feed, revealing that while the algorithm responds to signals, users struggle to effectively influence or stop certain content from appearing.

Contribution

The authors develop novel experimental techniques to study TikTok's FYP and analyze how explicit and implicit signals influence content personalization.

Findings

FYP algorithm responds to both explicit and implicit signals.

Users find it difficult to stop unwanted content due to interface design.

Once disinterest signals are removed, the feed often reverts to showing similar content.

Abstract

The short-form video-sharing service TikTok has become an important platform in the social media landscape, with much of its popularity owed to its algorithmically-driven "For You Page" (FYP). This feature serves as the "home screen" for the platform and provides a personalized feed of content for each user. Unlike other social media services-where new users start their journey by explicitly signaling whom they choose to friend or follow-the TikTok FYP algorithm instead begins making inferences based on implicit signals, such as how long they watch particular videos. As a result, users have less explicit control over what content they see, and concerns have been raised about the impact on users (e.g., the delivery of potentially harmful content). In this work, we investigate the extent to which users have control over the content they see on the FYP on TikTok. We first develop novel techniques to study the TikTok mobile app, introducing a new avenue for conducting controlled experiments that enable us to send both explicit and implicit signals on the app. We then use these techniques to study the FYP algorithm based on accounts we control. We find that the FYP algorithm is sensitive to both types of signals, changing the amount of personalized content the account sees. However, we find that users may have difficulty convincing the FYP algorithm to stop showing content the user wishes to no longer see: the most effective explicit signal-marking a video as 'Not Interested'-is unintuitively buried in the interface. Worse, we find that once accounts cease to indicate disinterest in a topic, many find their feeds dominated by such content again.