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.

翻译：社交媒体以前所未有的速度和规模推动了信息传播，同时也促使高参与度、低质量内容的泛滥。*摩擦*——即通过行为设计手段使内容分享变得更加繁琐——或可成为提升线上传播内容质量的一种途径。本文基于有无质量识别学习的对照实验，通过基于智能体模型的仿真研究揭示：单纯的摩擦干预虽能减少发帖数量，却未能改善其质量；而将少量摩擦与学习机制相结合，则能显著提升内容的平均质量。基于初步证据，我们提出一种融合平台社区标准学习的摩擦干预方案，并拟通过实地实验进行验证。该干预方案对用户参与度影响甚微，且易于大规模部署实施。