  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.
