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Many critics raise concerns about the prevalence of ‘echo chambers’ on social media and their potential role in increasing political polarization. However, the lack of available data and the challenges of conducting large-scale field experiments have made it difficult to assess the scope of the problem 1 , 2 . Here we present data from 2020 for the entire population of active adult Facebook users in the USA showing that content from ‘like-minded’ sources constitutes the majority of what people see on the platform, although political information and news represent only a small fraction of these exposures. To evaluate a potential response to concerns about the effects of echo chambers, we conducted a multi-wave field experiment on Facebook among 23,377 users for whom we reduced exposure to content from like-minded sources during the 2020 US presidential election by about one-third. We found that the intervention increased their exposure to content from cross-cutting sources and decreased exposure to uncivil language, but had no measurable effects on eight preregistered attitudinal measures such as affective polarization, ideological extremity, candidate evaluations and belief in false claims. These precisely estimated results suggest that although exposure to content from like-minded sources on social media is common, reducing its prevalence during the 2020 US presidential election did not correspondingly reduce polarization in beliefs or attitudes. A large-scale field intervention experiment on 23,377 US Facebook users during the 2020 presidential election shows that reducing exposure to content from like-minded social media sources has no measurable effect on political polarization or other political attitudes and beliefs.

The massive spread of digital misinformation has been identified as a major threat to democracies. Communication, cognitive, social, and computer scientists are studying the complex causes for the viral diffusion of misinformation, while online platforms are beginning to deploy countermeasures. Little systematic, data-based evidence has been published to guide these efforts. Here we analyze 14 million messages spreading 400 thousand articles on Twitter during ten months in 2016 and 2017. We find evidence that social bots played a disproportionate role in spreading articles from low-credibility sources. Bots amplify such content in the early spreading moments, before an article goes viral. They also target users with many followers through replies and mentions. Humans are vulnerable to this manipulation, resharing content posted by bots. Successful low-credibility sources are heavily supported by social bots. These results suggest that curbing social bots may be an effective strategy for mitigating the spread of online misinformation. Online misinformation is a threat to a well-informed electorate and undermines democracy. Here, the authors analyse the spread of articles on Twitter, find that bots play a major role in the spread of low-credibility content and suggest control measures for limiting the spread of misinformation.

The dynamics and influence of fake news on Twitter during the 2016 US presidential election remains to be clarified. Here, we use a dataset of 171 million tweets in the five months preceding the election day to identify 30 million tweets, from 2.2 million users, which contain a link to news outlets. Based on a classification of news outlets curated by www.opensources.co , we find that 25% of these tweets spread either fake or extremely biased news. We characterize the networks of information flow to find the most influential spreaders of fake and traditional news and use causal modeling to uncover how fake news influenced the presidential election. We find that, while top influencers spreading traditional center and left leaning news largely influence the activity of Clinton supporters, this causality is reversed for the fake news: the activity of Trump supporters influences the dynamics of the top fake news spreaders. The influence of 'fake news’, spread via social media, has been much discussed in the context of the 2016 US presidential election. Here, the authors use data on 30 million tweets to show how content classified as fake news diffused on Twitter before the election.

Public acceptance is a precondition for implementing taxes and laws aimed at mitigating climate change. However, it still remains challenging to understand its determinants for the climate community. Here, we use a meta-analytic approach to examine the role of public opinion about climate change taxes and laws. Fifteen variables were examined by synthesizing 89 datasets from 51 articles across 33 countries, with a total sample of 119,465 participants. Among all factors, perceived fairness and effectiveness were the most important determinants. Self-enhancement values and knowledge about climate change showed weak relationships and demographic variables showed only weak or close to zero effects. Our meta-analytic results provide useful insights and have the potential to inform climate change researchers, practitioners and policymakers to better design climate policy instruments.Although public acceptance is essential for effective climate policies, the underlying drivers are still not well understood. Now, meta-analysis results show that perceived fairness and effectiveness have the strongest relationships with support for climate taxes and laws.

There is widespread concern that foreign actors are using social media to interfere in elections worldwide. Yet data have been unavailable to investigate links between exposure to foreign influence campaigns and political behavior. Using longitudinal survey data from US respondents linked to their Twitter feeds, we quantify the relationship between exposure to the Russian foreign influence campaign and attitudes and voting behavior in the 2016 US election. We demonstrate, first, that exposure to Russian disinformation accounts was heavily concentrated: only 1% of users accounted for 70% of exposures. Second, exposure was concentrated among users who strongly identified as Republicans. Third, exposure to the Russian influence campaign was eclipsed by content from domestic news media and politicians. Finally, we find no evidence of a meaningful relationship between exposure to the Russian foreign influence campaign and changes in attitudes, polarization, or voting behavior. The results have implications for understanding the limits of election interference campaigns on social media. Here, using longitudinal survey and Twitter data, the authors examine the relationship between exposure to Russian Internet Research Agency activities on Twitter and voting behavior and attitudes in the 2016 US election.

In today’s polarized political climate, researchers who combat mistruths have come under attack and been labelled as unelected arbiters of truth. But the fight against misinformation is valid, warranted and urgently required. In today’s polarized political climate, researchers who combat mistruths have come under attack and been labelled as unelected arbiters of truth. But the fight against misinformation is valid, warranted and urgently required.

Russia’s invasion has caused a short-term spike in prices, but could prompt a long-term shift towards sustainability. Russia’s invasion has caused a short-term spike in prices, but could prompt a long-term shift towards sustainability. Credit: Andrey Rudakov/Bloomberg via Getty Gazprom PJSC's Nord Stream 2 Slavyanskaya Compressor Station in Ust-Luga, Russia.

Many recently updated climate models show greater future warming than previously. Separate lines of evidence suggest that their warming rates may be unrealistically high, but the risk of such eventualities only emphasizes the need for rapid and deep reductions in emissions.

In-depth interviews are a versatile form of qualitative data collection used by researchers across the social sciences. They allow individuals to explain, in their own words, how they understand and interpret the world around them. Interviews represent a deceptively familiar social encounter in which people interact by asking and answering questions. They are, however, a very particular type of conversation, guided by the researcher and used for specific ends. This dynamic introduces a range of methodological, analytical and ethical challenges, for novice researchers in particular. In this Primer, we focus on the stages and challenges of designing and conducting an interview project and analysing data from it, as well as strategies to overcome such challenges. In-depth interviews are a versatile form of qualitative data collection used by researchers across the social sciences. In this Primer, Knott et al. describe the stages and challenges involved in designing and conducting interviews, how to analyse the data and strategies to overcome challenges.

The 2030 Sustainable Development Goals (SDGs) aim at jointly improving economic, social, and environmental outcomes for human prosperity and planetary health. However, designing national economic policies that support advancement across multiple Sustainable Development Goals is hindered by the complexities of multi-sector economies and often conflicting policies. To address this, we introduce a national-scale design framework that can enable policymakers to sift through complex, non-linear, multi-sector policy spaces to identify efficient policy portfolios that balance economic, social, and environmental goals. The framework combines economy-wide sustainability simulation and artificial intelligence-driven multiobjective, multi-SDG policy search and machine learning. The framework can support multi-sector, multi-actor policy deliberation to screen efficient policy portfolios. We demonstrate the utility of the framework for a case study of Egypt by identifying policy portfolios that achieve efficient mixes of poverty and inequality reduction, economic growth, and climate change mitigation. The results show that integrated policy strategies can help achieve sustainable development while balancing adverse economic, social, and political impacts of reforms. Selecting economic policies to achieve sustainable development is challenging due to the many sectors involved and the trade-offs implied. Artificial intelligence combined with economy-wide computer simulations can help.