Friction Interventions to Curb the Spread of Misinformation on Social Media

TL;DR

This paper explores how behavioral friction measures, especially when combined with learning about community standards, can improve the quality of content shared on social media while maintaining user engagement.

Contribution

It introduces a novel approach of combining friction with learning to enhance content quality, supported by agent-based modeling and a proposed scalable intervention.

Findings

Friction alone reduces posting activity but doesn't improve quality.

Adding learning to friction significantly increases post quality.

Proposed intervention is minimally disruptive and scalable.

Abstract

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.