Becoming Intimate with Algorithms: Users’ Encounters, Imaginaries, and Affective Bonds with TikTok

Abstract

In an increasingly algorithm-driven digital landscape, personalization systems are often praised as almost magical entities capable of "knowing us better than we know ourselves". This study takes TikTok as a privileged object of investigation to explore how users engage with these algorithms that seek to "know" them. Adopting a qualitative, empirical approach, we conducted 20 semi-structured interviews with TikTok users in Brazil to explore their everyday experiences, perceptions, and imaginations around algorithmic recommendations. We argue that TikTok’s algorithm-centered infrastructure shapes a unique user experience, where navigating the platform is essentially about engaging in a relationship with the algorithm itself. Our participants not only demonstrated a high algorithmic awareness but developed informal theories and strategies to “train” their systems in an effort to refine recommendation accuracy. As these users experienced increasingly personalized content — deeply attuned to their emotions and personal experiences — some expressed a sense of algorithmic intimacy. Through their clicks, likes, searches, and interactions, they felt as if “sharing a secret with the algorithm”, revealing personal, sensitive, and sometimes even embarrassing preferences. In this preliminary analysis, we discuss how TikTok’s “For You” model fosters a new form of intimacy mediated by algorithms, data collection, and commercial interests. While the transformation of intimacy is not a new topic in internet studies, this paper shifts the focus from self-exposure on social media to the cultivation of an intimate relationship between users and algorithms, offering new insights into the affective and subjective dimensions of algorithmic personalization.