This article explores how Twitter’s algorithmic timeline influences exposure to different types of external media. We use an agent-based testing method to compare chronological timelines and algorithmic timelines for a group of Twitter agents that emulated real-world archetypal users. We first find that algorithmic timelines exposed agents to external links at roughly half the rate of chronological timelines. Despite the reduced exposure, the proportional makeup of external links remained fairly stable in terms of source categories (major news brands, local news, new media, etc.). Notably, however, algorithmic timelines slightly increased the proportion of “junk news” websites in the external link exposures. While our descriptive evidence does not fully exonerate Twitter’s algorithm, it does characterize the algorithm as playing a fairly minor, supporting role in shifting media exposure for end users, especially considering upstream factors that create the algorithm’s input—factors such as human behavior, platform incentives, and content moderation. We conclude by contextualizing the algorithm within a complex system consisting of many factors that deserve future research attention.

本文探讨了 Twitter 的算法时间线如何影响对不同类型的外部媒体的曝光。我们使用基于代理的测试方法来比较一组模拟真实世界原型用户的 Twitter 代理的时间顺序和算法时间线。我们首先发现算法时间线将代理暴露给外部链接的速度大约是时间线的一半。尽管曝光率有所下降，但在来源类别（主要新闻品牌、地方新闻、新媒体等）方面，外部链接的比例构成仍然相当稳定。然而，值得注意的是，算法时间线略微增加了外部链接曝光中“垃圾新闻”网站的比例。虽然我们的描述性证据并没有完全证明 Twitter 的算法无罪，但它确实将算法描述为一个相当轻微的，在为最终用户转移媒体曝光方面的支持作用，特别是考虑到创建算法输入的上游因素，例如人类行为、平台激励和内容审核。最后，我们将算法置于一个复杂系统中，该系统由许多值得未来研究关注的因素组成。