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We investigate the relationship between education and antisemitism using unique individual-level survey data on antisemitism from more than 100 countries. Our findings show that education is associated with greater favorability toward Jews, but the relationship between education and endorsement of antisemitic stereotypes and conspiracy theories varies between countries. In countries that actively supported recent statements condemning Holocaust denial and antisemitism at the United Nations—which we use as a proxy for country-level opposition to antisemitism in education and politics—greater education is associated with reduced endorsement of antisemitic stereotypes. By contrast, more educated people are more likely to endorse antisemitic stereotypes than less educated people in countries that declined to endorse those statements. These descriptive findings provide new evidence about the association between education and intolerance.

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.Using survey and internet browsing data and expert ratings, Bhadani et al. find that incorporating partisan audience diversity into algorithmic rankings of news websites increases the trustworthiness of the sites they recommend and maintains relevance.

When a party or candidate loses the popular vote but still wins the election, do voters view the winner as legitimate? This scenario, known as an electoral inversion, describes the winners of two of the last six presidential elections in the United States. We report results from two experiments testing the effect of inversions on democratic legitimacy in the US context. Our results indicate that inversions significantly decrease the perceived legitimacy of winning candidates. Strikingly, this effect does not vary with the margin by which the winner loses the popular vote, nor by whether the candidate benefiting from the inversion is a co-partisan. The effect is driven by Democrats, who punish inversions regardless of candidate partisanship; few effects are observed among Republicans. These results suggest that the experience of inversions increases sensitivity to such outcomes among supporters of the losing party.

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 U.S. citizens, 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.