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Newsfeed algorithms frequently amplify misinformation and other low-quality content. How can social media platforms more effectively promote reliable information? Existing approaches are difficult to scale and vulnerable to manipulation. In this paper, we propose using the political diversity of a website’s audience as a quality signal. Using news source reliability ratings from domain experts and web browsing data from a diverse sample of 6,890 US residents, we first show that websites with more extreme and less politically diverse audiences have lower journalistic standards. We then incorporate audience diversity into a standard collaborative filtering framework and show that our improved algorithm increases the trustworthiness of websites suggested to users—especially those who most frequently consume misinformation—while keeping recommendations relevant. These findings suggest that partisan audience diversity is a valuable signal of higher journalistic standards that should be incorporated into algorithmic ranking decisions. 

Does information about how other people feel about COVID-19 vaccination affect immunization intentions? We conducted preregistered survey experiments in Great Britain (5,456 respondents across 3 survey waves from September 2020 to February 2021), Canada (1,315 respondents in February 2021), and the state of New Hampshire in the United States (1,315 respondents in January 2021). The experiments examine the effects of providing accurate public opinion information to people about either public support for COVID-19 vaccination (an injunctive norm) or public beliefs that the issue is contentious. Across all 3 countries, exposure to this information had minimal effects on vaccination intentions even among people who previously held inaccurate beliefs about support for COVID-19 vaccination or its perceived contentiousness. These results suggest that providing information on public opinion about COVID vaccination has limited additional effect on people’s behavioral intentions when public discussion of vaccine uptake and intentions is highly salient.

 

The widespread availability of voter files has improved the study of participation in American politics, but the lack of comprehensive data on nonregistrants creates difficult inferential issues. Most notably, observational studies that examine turnout rates among registrants often implicitly condition on registration, a posttreatment variable that can induce bias if the treatment of interest also affects the likelihood of registration. We introduce a sensitivity analysis to assess the potential bias induced by this problem, which we call differential registration bias. Our approach is most helpful for studies that estimate turnout among registrants using posttreatment registration data, but it is also valuable for studies that estimate turnout among the voting‐eligible population using secondary sources. We illustrate our approach with two studies of voting eligibility effects on subsequent turnout among young voters. In both cases, eligibility appears to decrease turnout, but these effects are found to be highly sensitive to differential registration bias.