TikTok users don’t have as much agency over their FYPs as they think

TikTok 用户对“为你推荐”（FYP）的掌控力远没有他们想象中那么大

TikTok’s For You Page (FYP) is the default home screen for users of the video-sharing platform. It’s a personalized, algorithmically driven content feed, but the approach differs from other social media in that TikTok’s algorithm relies heavily on implicit signals—such as how long users watch particular videos—as well as explicit signals such as likes or follows. And generally, that algorithm does remarkably well at predicting which videos will interest particular users.

TikTok 的“为你推荐”（FYP）是该视频分享平台用户的默认主屏幕。这是一个由算法驱动的个性化内容流，但其运作方式与其他社交媒体不同：TikTok 的算法不仅依赖点赞或关注等显性信号，还高度依赖隐性信号，例如用户观看特定视频的时长。总体而言，该算法在预测哪些视频会吸引特定用户方面表现得非常出色。

But some users have voiced concerns that TikTok’s almighty algorithm doesn’t seem to incorporate negative feedback very well. Even when they don’t watch a suggested video or click the “not interested” feature, they keep seeing those videos on their FYP. Northwestern University computer scientists put those suspicions to the test. According to their recent paper, the engagement signals do have an effect, but only temporarily. Then the algorithm gradually relapses unless a user consistently gives the same feedback over and over again.

但一些用户表示担忧，认为 TikTok 无所不能的算法似乎无法很好地采纳负面反馈。即使他们不观看推荐视频或点击“不感兴趣”功能，这些视频仍会不断出现在他们的 FYP 上。西北大学的计算机科学家们对这些怀疑进行了测试。根据他们最近发表的论文，互动信号确实有效，但只是暂时的。除非用户反复持续地给出相同的反馈，否则算法会逐渐“复发”。

The research group specializes in “algorithm audits,” co-author Piotr Sapiezynski told Ars, to better understand online platforms: “how they work, how they fail, when they fail, how they harm individuals and societies.” In this case, he and his co-authors wanted to take a closer look at user agency after hearing multiple anecdotal reports from TikTok users that their negative feedback—responding to prompts by indicating they aren’t interested or want to see less of a certain kind of video—doesn’t seem to remove those posts from their FYP. “On the other hand, it’s unclear why the platforms would offer it, if it doesn’t work,” said Sapiezynski.

合著者 Piotr Sapiezynski 告诉 Ars，该研究小组专门从事“算法审计”，旨在更好地了解在线平台：“它们如何运作、如何失效、何时失效，以及它们如何伤害个人和社会。” 在这项研究中，他和合著者们在听到多位 TikTok 用户的轶事报告后，想要深入探究用户的自主权。用户们反映，他们的负面反馈（即通过提示表示对某类视频不感兴趣或希望减少观看）似乎并不能将这些内容从 FYP 中移除。“另一方面，如果这些功能不起作用，平台为什么要提供它们，这一点尚不清楚，”Sapiezynski 说道。

Their methodology did not involve computer simulations; rather, they created bot accounts on the actual TikTok mobile app, rather than studying actual users. “We used emulated devices, where we are creating accounts and automatically interfering with the TikTok algorithm through code with the sock puppet accounts,” co-author Levi Kaplan told Ars. “We’ve come up with a methodology where we get the metadata by intercepting the network traffic, and then we make a decision using an LLM. All the LLMs were validated with human responses as well.”

他们的研究方法不涉及计算机模拟；相反，他们在真实的 TikTok 移动应用上创建了机器人账号，而不是研究真实用户。“我们使用了模拟设备，通过代码控制这些‘傀儡账号’来创建账户，并自动干扰 TikTok 的算法，”合著者 Levi Kaplan 告诉 Ars。“我们开发了一种方法，通过拦截网络流量获取元数据，然后利用大语言模型（LLM）做出决策。所有大语言模型也都经过了人类反馈的验证。”

“We basically work from the assumption that if we want data, then we need to obtain it ourselves,” Sapiezynski said of their account cloning approach. “Even if we did, for example, want to use the official TikTok researcher API, none of the user agency is covered there. You can see what content is available, but you cannot see individual timelines that will tell you how the algorithm reacts to a particular user watching or not watching a particular video. Similarly, with the European Union’s researcher data access, all of this data can only be accessed aggregated and not from a perspective of a single user. So when you want to really study personalization, this research cannot be done on the aggregated data.”

“我们基本上基于这样一个假设：如果我们想要数据，就必须自己获取，”Sapiezynski 在谈到他们的账号克隆方法时说。“即使我们想使用 TikTok 官方的研究人员 API，其中也不包含任何关于用户自主权的内容。你可以看到有哪些内容，但无法看到个人时间线，无法得知算法如何对特定用户观看或不观看特定视频做出反应。同样，欧盟的研究人员数据访问权限也只能获取汇总数据，而非单个用户的视角。因此，当你想要真正研究个性化时，这项研究无法通过汇总数据来完成。”

The team ran their experiments multiple times on the 90 cloned accounts and made side-by-side comparisons, using both implicit and explicit signals, to see how TikTok’s algorithm responded in terms of recommended content on the FYPs. They focused on three popular topics: cooking videos, fitness videos, and sports betting. The “not interested” button proved most effective, reducing unwanted content by around 84 percent, compared to just a 48 percent reduction from merely skipping videos. “So if you don’t want to see something, you should be hitting that button,” said Kaplan.

研究团队在 90 个克隆账号上多次进行了实验，并进行了横向对比，同时使用隐性和显性信号，以观察 TikTok 算法在 FYP 推荐内容方面的反应。他们重点关注了三个热门话题：烹饪视频、健身视频和体育博彩。“不感兴趣”按钮被证明最有效，能减少约 84% 的不想要的内容，而仅仅跳过视频只能减少 48%。Kaplan 说：“所以如果你不想看到某些内容，你应该点击那个按钮。”

But the authors note that the “not interested” option seems to be deliberately hidden from users. And it was very easy for the algorithm to “relapse” into once again flooding an FYP with previously unwanted content; even a brief re-engagement by a user is sufficient. “It turns out that it works in the beginning,” said Sapiezynski. “When you start saying, ‘I don’t want to see this particular topic,’ the platform might actually show you fewer of such pieces of content. But then the platform will slowly start putting it back in your feed. And if you don’t continue saying, ‘I really don’t want to see it,’ this may balloon back to the place where it was in the beginning. So the platform does react to your negative feedback, but then it also very much reacts to your express behavior. So if you are presented with this content again and you start watching it, the platform will again feed it to you more and more.”

但作者指出，“不感兴趣”选项似乎被刻意隐藏了起来。而且算法非常容易“复发”，再次用之前不想要的内容充斥用户的 FYP；即使是用户短暂的重新互动也足以导致这种情况。Sapiezynski 说：“事实证明，它在开始时是有效的。当你开始说‘我不想看这个特定话题’时，平台确实可能会减少向你展示此类内容。但随后，平台会慢慢开始将其放回你的信息流中。如果你不持续表示‘我真的不想看它’，这种情况可能会膨胀回最初的状态。所以，平台确实会对你的负面反馈做出反应，但它同时也非常看重你的实际行为。因此，如果你再次看到这些内容并开始观看，平台就会再次越来越多地向你推送。”

In other words, be consistently very active with your feedback—constant vigilance!—when it comes to curating TikTok’s FYP. The researchers hope to test this hypothesis on real user data in the future. That said, “We can teach users how to use the platform better, but ultimately the way that you’re interfacing with the platform is going to be dictated by the design decisions that are fundamental to the platform,” said Kaplan.

换句话说，在管理 TikTok 的 FYP 时，你需要持续且积极地给出反馈——时刻保持警惕！研究人员希望未来能在真实用户数据上验证这一假设。尽管如此，Kaplan 表示：“我们可以教用户如何更好地使用该平台，但归根结底，你与平台交互的方式将由平台最根本的设计决策所决定。”