论文标题
从病毒内容中学习
Learning from Viral Content
论文作者
论文摘要
我们通过与共享新闻报道互动的用户平衡模型在社交媒体上学习学习。理性用户依次到达，观察一个原始故事（即私人信号）和新闻源中的前辈故事的样本，然后决定要分享哪些故事。观察到的故事样本取决于前任共享的内容以及生成新闻提要的抽样算法。我们专注于该算法选择更多病毒式（即广泛共享）故事的频率。向用户展示病毒故事可以增加信息的聚合，但它也可以产生大多数共享故事错误的稳态。这些误导性的稳态自我延续，因为观察到错误的故事的用户会发展错误的信念，因此在理性上继续分享它们。最后，我们描述了平台设计和鲁棒性的几个后果。
We study learning on social media with an equilibrium model of users interacting with shared news stories. Rational users arrive sequentially, observe an original story (i.e., a private signal) and a sample of predecessors' stories in a news feed, and then decide which stories to share. The observed sample of stories depends on what predecessors share as well as the sampling algorithm generating news feeds. We focus on how often this algorithm selects more viral (i.e., widely shared) stories. Showing users viral stories can increase information aggregation, but it can also generate steady states where most shared stories are wrong. These misleading steady states self-perpetuate, as users who observe wrong stories develop wrong beliefs, and thus rationally continue to share them. Finally, we describe several consequences for platform design and robustness.
