  Social media has enabled the spread of information at unprecedented speeds
and scales, and with it the proliferation of high-engagement, low-quality
content. *Friction* -- behavioral design measures that make the sharing of
content more cumbersome -- might be a way to raise the quality of what is
spread online. Here, we study the effects of friction with and without
quality-recognition learning. Experiments from an agent-based model suggest
that friction alone decreases the number of posts without improving their
quality. A small amount of friction combined with learning, however, increases
the average quality of posts significantly. Based on this preliminary evidence,
we propose a friction intervention with a learning component about the
platform's community standards, to be tested via a field experiment. The
proposed intervention would have minimal effects on engagement and may easily
be deployed at scale.
