There is widespread concern that Russia and other countries have launched social-media campaigns designed to increase political divisions in the United States. Though a growing number of studies analyze the strategy of such campaigns, it is not yet known how these efforts shaped the political attitudes and behaviors of Americans. We study this question using longitudinal data that describe the attitudes and online behaviors of 1,239 Republican and Democratic Twitter users from late 2017 merged with nonpublic data about the Russian Internet Research Agency (IRA) from Twitter. Using Bayesian regression tree models, we find no evidence that interaction with IRA accounts substantially impacted 6 distinctive measures of political attitudes and behaviors over a 1-mo period. We also find that interaction with IRA accounts were most common among respondents with strong ideological homophily within their Twitter network, high interest in politics, and high frequency of Twitter usage. Together, these findings suggest that Russian trolls might have failed to sow discord because they mostly interacted with those who were already highly polarized. We conclude by discussing several important limitations of our study—especially our inability to determine whether IRA accounts influenced the 2016 presidential election—as well as its implications for future research on social media influence campaigns, political polarization, and computational social science.
When it comes to knowledge of demographic facts, misinformation appears to be the norm. Americans massively overestimate the proportions of their fellow citizens who are immigrants, Muslim, LGBTQ, and Latino, but underestimate those who are White or Christian. Previous explanations of these estimation errors have invoked topic-specific mechanisms such as xenophobia or media bias. We reconsidered this pattern of errors in the light of more than 30 years of research on the psychological processes involved in proportion estimation and decision-making under uncertainty. In two publicly available datasets featuring demographic estimates from 14 countries, we found that proportion estimates of national demographics correspond closely to what is found in laboratory studies of quantitative estimates more generally. Biases in demographic estimation, therefore, are part of a very general pattern of human psychology-independent of the particular topic or demographic under consideration-that explains most of the error in estimates of the size of politically salient populations. By situating demographic estimates within a broader understanding of general quantity estimation, these results demand reevaluation of both topic-specific misinformation about demographic facts and topic-specific explanations of demographic ignorance, such as media bias and xenophobia.
A large literature demonstrates that conservatives have greater needs for certainty than liberals. This suggests an asymmetry hypothesis: Conservatives are less open to new information that conflicts with their political identity and, in turn, political accountability will be lower on the right than the left. However, recent work suggests that liberals and conservatives are equally prone to politically motivated reasoning (PMR). The present article confronts this puzzle. First, we identify significant limitations of extant studies evaluating the asymmetry hypothesis and deploy two national survey experiments to address them. Second, we provide the first direct test of the key theoretical claim underpinning the asymmetry hypothesis: epistemic needs for certainty promote PMR. We find little evidence for the asymmetry hypothesis. Importantly, however, we also find no evidence that epistemic needs promote PMR. That is, although conservatives report greater needs for certainty than liberals, these needs are not a major source of political bias.
Recent years have seen a proliferation of experiments seeking to combat misinformation. Yet there has been little consistency across studies in how the effectiveness of interventions is evaluated, which undermines the field's ability to identify efficacious strategies. We provide a framework for differentiating between common research designs on the basis of the normative claims they make about how people should interact with information. We recommend an approach that aligns with the normative claim that citizens should maximize the accuracy of the content they believe and share, which requires (i) a design that includes both true and false content, and (ii) an analysis that includes examining discernment between the two. Using data from recent misinformation studies, we show that using the wrong research design can lead to misleading conclusions about who is most likely to spread misinformation and how to stop it.
The spread of misinformation has become a central concern in American politics. Recent studies of social media sharing suggest that Republicans are considerably more likely to share fake news than Democrats. However, such inferences are confounded by the far greater supply of right-leaning fake news—Republicans may indeed be more prone to sharing fake news, or they may simply be more exposed to it. This article disentangles these competing explanations by examining sharing intentions in a balanced information environment. Using a large national survey of YouGov respondents, we show that Republicans are indeed more prone to sharing ideologically concordant fake news than Democrats, but that this gap is not large enough to explain differences in sharing observed online. Encouragingly, however, we also find that accuracy prompt interventions that reduce the spread of fake news are equally effective across parties, suggesting that fake news sharing among Republicans is not an intractable problem.
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