Current models rely on engagement metrics like dwell time and click-through rates (CTR). High-arousal content—anger, outrage, or fear—triggers dopamine responses that keep users scrolling. This creates a feedback loop where sensationalism wins. To shift toward truth, platforms must change what they optimize for.
One way involves shifting from engagement to "value-based metrics." Instead of counting how many people clicked a link, algorithms could weight content based on source credibility or cross-referencing with verified databases. This is technically difficult. It requires massive computational power to vet claims in real-time. However, if platforms adjust their auction systems so that high-quality, verified information earns better ad placements, the financial incentive shifts. Advertisers hate being placed next to misinformation that harms their brand reputation.
Another approach focuses on friction. The current system is too frictionless. Slowing down the spread of unverified information by adding "speed bumps"—such as prompts asking if a user actually read an article—can reduce viral falsehoods. It might lower total time spent on site, but it builds long-term trust. If users view a platform as a reliable tool rather than a chaotic noise machine, they become more predictable and valuable to premium advertisers.
