Users struggle to guess TikTok video popularity without visible metrics
Echoes in the Algorithm: Analyzing the Fidelity of User Preferences Against Realized Platform Reach
Human-Computer Interaction
Summary
People on TikTok try to tell which videos are most popular even when likes and view counts are hidden. The authors studied this by having people compare pairs of videos and guess which had more views, and which they preferred. They found that people guessed the more popular video only a bit better than chance, and their preferences didn’t strongly match actual popularity. This means people rely on personal taste or weak clues rather than clear signals when popularity numbers aren’t shown.
What this means in practice
- •For social media platform designers: Adjust interface features to reduce visible popularity metrics without leaving users confused about content reach or popularity.
- •For digital marketing teams: Understand that removing visible popularity cues weakens users’ ability to identify trending content, impacting engagement strategies.
Authors
Emelia Hughes, Tim Weninger
Abstract
What does popular content look like when platforms withhold the usual cues? On TikTok, users still form impressions about which videos are taking off even when likes and view counts are hidden, delayed, or pushed to the margins of the interface. We study this problem through TokOrNot, a web-based game in which participants compared pairs of TikTok videos and reported (i) which one they preferred and (ii) which one they believed had reached a larger audience. We benchmark these judgments against verified public view counts, which we use as a bounded proxy for realized platform reach. Across 3,513 judgments from 363 participants, participants identified the higher-reach video only modestly above chance (56.75%, 95% CI: 56.01-58.55). Preference aligned with the higher-view video at a similar rate, while preference and prediction matched in 83.48% of trials (95% CI: 83.12-85.95). Performance also varied across content categories. Taken together, these results do not suggest that users can reliably read platform success from content alone. Instead, they point to a looser and more uncertain interpretive process in which reach judgments often track personal taste or other weak heuristics when explicit popularity cues are absent. We discuss the implications for algorithmic literacy and for interface designs that reduce visible metrics without leaving users to infer reach from uneven or idiosyncratic cues alone.