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#ForYou: The User’s Perspective on How TikTok Recommends Videos

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posted on 2020-12-18, 23:36 authored by Marco ScalviniMarco Scalvini

The current study is aimed at understanding the impact of TikTok’s recommendation system. The algorithm is perceived as very efficient in targeting users but raises several ethical concerns regarding the ability to manipulate users’ experience and the extent to which private data and preferences are respected. Utilizing the data collected from 40 in-depth interviews, this study explores: How do users perceive TikTok’s ethical responsibilities in regard to their algorithmic recommendation system? Furthermore, the analysis discusses and evaluates the tension between a) how the platform’s algorithm feeds users similar videos that they highly appreciate; and, inversely, b) how the diversification of recommendations is limited. A thematic analysis shows interviewees describe TikTok as a safe space where users can be themselves and feel included in a community of people interested in posting content to connect and engage meaningfully beyond difference. However, the algorithm is perceived as harmful because it tries to manipulate and drive users towards specific videos that increase their ‘addiction’ to the platform. Interviewees consider some of the recommendations on the ForYou page to be questionable because they aimed at persuading or nudging in favor of particular hashtags and social causes. This contradiction may partly be explained by the fact that interviewees report their rationalizations in a performative manner in order to avoid feelings of dissonance while attempting to relate to their own self-identity. This observation leads to the idea that the concept of mediated diversity can explain the tension between the expectation of similarity and diversity.


Declaration of conflicts of interest

No Conflict

Corresponding author email

Lead author country

  • Netherlands

Lead author job role

  • Higher Education Lecturer

Human Participants

  • Yes

Ethics statement

No sensitive data, Informed consent obtained


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