Social media has enabled the spread of information at unprecedented speeds and scales, and withit 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 as it does not require labeling of content or detection of bad
actors.