Echoes in the Algorithm: Analyzing the Fidelity of User Preferences Against Realized Platform Reach

2026-09-08

AI Summary

研究通过TokOrNot游戏分析用户在缺少明确流行度提示时对TikTok视频受欢迎程度的判断准确性，发现用户仅能以略高于随机的概率识别高传播量视频。

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.

Problem

• user preference
• platform reach
• popularity cues

Innovation

• user preference
• platform reach
• algorithmic literacy
• content analysis