AI 中文总结
本研究通过TokNot游戏实验发现，在隐藏点赞和观看数时，用户判断视频触达的准确率仅略高于随机水平，且偏好与预测一致性有限，表明用户难以仅凭内容可靠解读平台成功。
AI 中文摘要
当平台隐藏惯常的提示时，热门内容会是什么样子？在TikTok上，即使点赞数和观看次数被隐藏、延迟或推到界面的边缘，用户仍然会对哪些视频正在走红形成印象。我们通过TokOrNot（一个基于网页的游戏）来研究这个问题，参与者比较成对的TikTok视频，并报告（i）他们更喜欢哪一个，以及（ii）他们认为哪一个吸引了更广泛的受众。我们将这些判断与经过验证的公开观看次数进行基准比较，将其用作实现平台触达的有界代理。在来自363名参与者的3513次判断中，参与者识别出较高触达视频的比例仅略高于随机水平（56.75%，95%置信区间：56.01-58.55）。偏好与较高观看次数视频的一致性处于相似水平，而偏好与预测在83.48%的试验中匹配（95%置信区间：83.12-85.95）。表现也因内容类别而异。综合来看，这些结果并不表明用户能够可靠地从内容本身解读平台成功。相反，它们指向一个更松散、更不确定的解读过程，在缺乏显式流行度提示时，触达判断往往跟随个人品味或其他弱启发式。我们讨论了对算法素养以及界面设计的影响，这些设计减少了可见指标，却不至于让用户仅从不均匀或特殊的线索中推断触达。
英文摘要
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.
