Traditional publishers invested in editorial standards, fact-checking, and professional journalism that served democratic functions even when pursuing commercial objectives. Platforms arbitrage this investment by surfacing publisher content through algorithms that strip away editorial context and optimize for engagement over accuracy.
The 2024 presidential election featured widespread misinformation alongside legitimate journalism. But algorithmic curation on platforms like X doesn’t distinguish between these based on editorial standards. Content that generates engagement gets amplified regardless of source quality, creating conditions where professional journalism competes on equal footing with propaganda.
The research examined over 1,000 users whose feeds were manipulated to include more or less divisive content. Much of this divisive content likely came from sources that wouldn’t meet traditional editorial standards, yet algorithms amplified it based on engagement rather than quality. This arbitrage allows platforms to profit from attention directed toward content they didn’t invest in creating or verifying.
Publishers who invest in quality journalism lose algorithmic competition to those who optimize for engagement through divisiveness and emotional manipulation. Over time, this economic pressure pushes publishers toward engagement-optimized content even when it conflicts with editorial standards, degrading information quality across entire media ecosystems.
Addressing this arbitrage might require different policy approaches. Platforms could be required to give algorithmic preference to editorially-verified content. They could be required to fund journalism through revenue sharing. They could be classified as publishers themselves, making them liable for amplified content. Or entirely different business models could emerge that realign incentives between information quality and commercial success.