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.
@article{arxiv.2307.11498,
title = {Friction Interventions to Curb the Spread of Misinformation on Social Media},
author = {Laura Jahn and Rasmus K. Rendsvig and Alessandro Flammini and Filippo Menczer and Vincent F. Hendricks},
journal= {arXiv preprint arXiv:2307.11498},
year = {2023}
}