Working Paper

Abstract

Social media platforms increasingly use consumption signals to rank content. Instead of relying on network based measures or predicted engagement (ie, likes or retweets), platforms simply optimize for whether users are likely to spend more time consuming content. We propose this leads to a phenomenon called digital rubbernecking, where content that is attention-grabbing because of toxicity or negativity is more likely to be surfaced in platforms that optimize for consumption. We test this using a simulated social media environment and custom-built ranking algorithms that either prioritize consumption or engagement signals.