Collective incentives improve group accuracy by reducing reliance on shared news sources

Research on online news consumption has long focused on improving people’s ability to select accurate news sources. However, people who consume accurate information do not necessarily make an accurate collective. Reliable information aggregation and collective decision-making also benefit from independence between judgments. We tested whether collective incentives can improve collective accuracy by reducing judgment correlation, even when average individual accuracy remains unchanged. We define collective incentives as reward structures that tie an individual’s payoff to the accuracy of their group’s aggregated decision rather than their own personal accuracy. In an online experiment ( N = 232), participants made forecasts after viewing realistic tweets from correlated or independent news sources. Shared tweets among group members were more accurate but fully correlated across group members; local tweets were less accurate but independent between participants. When rewarded for collective accuracy, participants used more independent local tweets, improving collective but not individual accuracy. Generalizing our findings, an exploratory analysis of public news datasets found a greater content correlation among larger publishers, modulated by accuracy. These findings highlight the overlooked importance of judgment independence alongside accuracy in creating thriving information ecologies online. Collective incentives may complement accuracy-based interventions by nudging news consumption towards diverse, local, and independent sources. We discuss how future research should implement collective incentives to improve collective decisions in real-world settings.