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

• URL: http://arxiv.org/abs/2605.10690v1
• Date: Mon, 11 May 2026 15:03:10 GMT
• Title: When 'For You' Isn't For You: Measuring User Agency in TikTok's Algorithmic Feed
• Abstract summary: We study the extent to which users have control over the content they see on the TikTok app.<n>We find that the FYP algorithm is sensitive to both types of signals, changing the amount of personalized content the account sees.
• Score: 1.283645731622868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Using AI Agents to Automate Black-Box Audits of Personalization Algorithms at Scale [76.72846830885882]
  Personalization algorithms determine what content users encounter on online platforms.<n> auditing these systems is difficult because independent auditors have only black-box access to the algorithms.<n>We introduce a framework for black-box audits of personalization algorithms using generative AI agents as behavioral engines for synthetic accounts.
  arXiv  Detail & Related papers  (2026-06-29T18:25:09Z)
• Learning Next Action Predictors from Human-Computer Interaction [53.41504288073184]
  LongNAP is a user model that combines parametric and in-context learning to reason over long interaction histories.<n>We label over 360K actions across one month of continuous phone usage from 20 users, amounting to 1,800 hours of screen time.<n>LongNAP significantly outperforms supervised finetuning and prompted baselines on held-out data.
  arXiv  Detail & Related papers  (2026-03-06T05:31:04Z)
• Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content [66.71102704873185]
  We test for user strategization by conducting a lab experiment and survey. We find strong evidence of strategization across outcome metrics, including participants' dwell time and use of "likes" Our findings suggest that platforms cannot ignore the effect of their algorithms on user behavior.
  arXiv  Detail & Related papers  (2024-05-09T07:36:08Z)
• User Strategization and Trustworthy Algorithms [81.82279667028423]
  We show that user strategization can actually help platforms in the short term. We then show that it corrupts platforms' data and ultimately hurts their ability to make counterfactual decisions.
  arXiv  Detail & Related papers  (2023-12-29T16:09:42Z)
• Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donations [31.764672446151412]
  We analyze user engagement on TikTok using data we collect via a data donation system. We find that the average daily usage time increases over the users' lifetime while the user attention remains stable at around 45%. We also find that users like more videos uploaded by people they follow than those recommended by people they do not follow.
  arXiv  Detail & Related papers  (2023-01-12T11:34:45Z)
• Modeling Content Creator Incentives on Algorithm-Curated Platforms [76.53541575455978]
  We study how algorithmic choices affect the existence and character of (Nash) equilibria in exposure games. We propose tools for numerically finding equilibria in exposure games, and illustrate results of an audit on the MovieLens and LastFM datasets.
  arXiv  Detail & Related papers  (2022-06-27T08:16:59Z)
• An Empirical Investigation of Personalization Factors on TikTok [77.34726150561087]
  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. 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. We identify that the follow-feature has the strongest influence, followed by the like-feature and video view rate.
  arXiv  Detail & Related papers  (2022-01-28T17:40:00Z)
• Fatigue-aware Bandits for Dependent Click Models [11.887684896043883]
  We consider an online learning setting where a platform learns a policy to recommend content that takes user fatigue into account. For each piece of content, its attractiveness to a user depends on its intrinsic relevance and a discount factor which measures how many similar contents have been shown. Based on user's feedback, the platform learns the relevance of the underlying content as well as the discounting effect due to content fatigue.
  arXiv  Detail & Related papers  (2020-08-22T02:18:15Z)
• Federated Learning of User Authentication Models [69.93965074814292]
  We propose Federated User Authentication (FedUA), a framework for privacy-preserving training of machine learning models. FedUA adopts federated learning framework to enable a group of users to jointly train a model without sharing the raw inputs. We show our method is privacy-preserving, scalable with number of users, and allows new users to be added to training without changing the output layer.
  arXiv  Detail & Related papers  (2020-07-09T08:04:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.