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

2026-05-11

AI Summary

This study addresses the limited user control over TikTok’s “For You Page” (FYP) recommendation algorithm, which often results in the repeated exposure to unwanted content. The authors present the first controlled experimental framework tailored to the TikTok mobile application, leveraging automated accounts to simulate both explicit user signals (e.g., “Not Interested”) and implicit behaviors (e.g., skipping or dwell time). By integrating content categorization with metrics of personalization strength, the work systematically evaluates users’ ability to influence their recommendation feeds. Findings reveal that the FYP algorithm responds to both signal types; however, the “Not Interested” feature suffers from poor discoverability and transient efficacy—unwanted content quickly reappears once user interaction ceases. These results demonstrate that current interface design substantially constrains user agency in shaping their content experience.

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.

Problem

• user agency
• algorithmic feed
• content control
• TikTok
• recommendation algorithm

Innovation

• controlled experimentation
• algorithmic agency
• implicit signals
• explicit signals
• recommendation systems