Political Polarization

Measuring the impact of online misinformation is challenging. Traditional measures, such as user views or shares on social media, are incomplete because not everyone who is exposed to misinformation is equally likely to believe it. To address this issue, we developed a method that combines survey data with observational Twitter data to probabilistically estimate the number of users both exposed to and likely to believe a specific news story. As a proof of concept, we applied this method to 139 viral news articles and find that although false news reaches an audience with diverse political views, users who are both exposed and receptive to believing false news tend to have more extreme ideologies. These receptive users are also more likely to encounter misinformation earlier than those who are unlikely to believe it. This mismatch between overall user exposure and receptive user exposure underscores the limitation of relying solely on exposure or interaction data to measure the impact of misinformation, as well as the challenge of implementing effective interventions. To demonstrate how our approach can address this challenge, we then conducted data-driven simulations of common interventions used by social media platforms. We find that these interventions are only modestly effective at reducing exposure among users likely to believe misinformation, and their effectiveness quickly diminishes unless implemented soon after misinformation’s initial spread. Our paper provides a more precise estimate of misinformation’s impact by focusing on the exposure of users likely to believe it, offering insights for effective mitigation strategies on social media.

In most advanced democracies, concerns about the spread of misinformation are typically associated with feed-based social media platforms like Twitter and Facebook. These platforms also account for the vast majority of research on the topic. However, in most of the world, particularly in Global South countries, misinformation often reaches citizens through social media messaging apps, particularly WhatsApp. To fill the resulting gap in the literature, we conducted a multimedia deactivation experiment to test the impact of reducing exposure to potential sources of misinformation on WhatsApp during the weeks leading up to the 2022 Presidential election in Brazil. We find that this intervention significantly reduced participants’ exposure to false rumors circulating widely during the election. However, consistent with theories of mass media minimal effects, a short-term reduction in exposure to misinformation ahead of the election did not lead to significant changes in belief accuracy, political polarization, or well-being.