Collective learning in news consumption
In a complex digital space---where information is shared without vetting from central authorities and where emotional content, rather than factual veracity, better predicts content spread---individuals often need to learn through experience which news sources to trust and rely on. Although public and experts' intuition alike call for stronger scrutiny of public information providers, and reliance on global trusted outlets, there is a statistical argument to be made that counter these prescriptions. We consider the scenario in which news statements are used by individuals to achieve a collective payoff---as is the case in many electoral contexts. In this case, a plurality of independent though less accurate news providers might be better for the public good than having fewer highly accurate ones. In a carefully controlled experiment, we asked people to make binary forecasts and rewarded them for their individual or collective performance. In accordance with theoretical expectations, we found that when collectively rewarded people learned to rely more on local information sources and that this strategy accrued better collective performance. Importantly, these effects positively scaled with group size so that larger groups benefited more from trusting local news sources. We validate these claims against a real-world news dataset. These findings show the importance of independent (instead of simply accurate) voices in any information landscape, but particularly when large groups of people want to maximize their collective payoff. These results suggest---at least statistically speaking---that emphasizing collective payoffs in large networks of news end-users might foster resilience to collective information failures.