论文标题
部分可观测时空混沌系统的无模型预测
Birdwatch: Crowd Wisdom and Bridging Algorithms can Inform Understanding and Reduce the Spread of Misinformation
论文作者
论文摘要
我们提出了一种对社交媒体帖子选择客观信息和主观上有用的注释的方法。我们从在线环境中借鉴了贡献者注释错误信息并同时对他人的贡献进行评分的数据。我们的算法使用基于矩阵的方法（MF）方法来识别在异质用户组中广泛吸引人的注释，有时被称为“基于桥接的排名”。我们将这些数据与调查实验配对，其中将个体随机分配以查看帖子注释。我们发现，与总体平均水平和人群生成的基准相比，该算法选择的注释改善了关键指标。此外，当部署在Twitter上时，通过这种基于桥接的方法选择注释的人比没有看到注释的人更少重新制作社交媒体帖子。
We present an approach for selecting objectively informative and subjectively helpful annotations to social media posts. We draw on data from on an online environment where contributors annotate misinformation and simultaneously rate the contributions of others. Our algorithm uses a matrix-factorization (MF) based approach to identify annotations that appeal broadly across heterogeneous user groups - sometimes referred to as "bridging-based ranking." We pair these data with a survey experiment in which individuals are randomly assigned to see annotations to posts. We find that annotations selected by the algorithm improve key indicators compared with overall average and crowd-generated baselines. Further, when deployed on Twitter, people who saw annotations selected through this bridging-based approach were significantly less likely to reshare social media posts than those who did not see the annotations.
