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We used text analyzers to compare the language used in two recently published reports on the physical science of climate change: one authored by the Intergovernmental Panel on Climate Change (IPCC) and the other by the Nongovernmental International Panel on Climate Change (NIPCC; a group of prominent skeptics, typically with prior scientific training, organized by the Heartland Institute). Although both reports represent summaries of empirical research within the same scientific discipline, our language analyses revealed consistent and substantial differences between them. Most notably, the IPCC authors used more cautious (as opposed to certain) language than the NIPCC authors. This finding (among others) indicates that, contrary to that which is commonly claimed by skeptics, IPCC authors were actually more conservative in terms of language style than their NIPCC counterparts. The political controversy over climate change may cause proponents' language to be conservative (for fear of being attacked) and opponents' language to be aggressive (to more effectively attack). This has clear implications for the science communication of climate research.

A meaningful portion of online misinformation sharing is likely attributable to Internet users failing to consider accuracy when deciding what to share. As a result, simply redirecting attention to the concept of accuracy can increase sharing discernment. Here we discuss the importance of accuracy and describe a limited-attention utility model that is based on a theory about inattention to accuracy on social media. We review research that shows how a simple nudge or prompt that shifts attention to accuracy increases the quality of news that people share (typically by decreasing the sharing of false content), and then discuss outstanding questions relating to accuracy nudges, including the need for more work relating to persistence and habituation as well as the dearth of cross-cultural research on these topics. We also make several recommendations for policy-makers and social media companies for how to implement accuracy nudges.

Many people consume news via social media. It is therefore desirable to reduce social media users' exposure to low-quality news content. One possible intervention is for social media ranking algorithms to show relatively less content from sources that users deem to be untrustworthy. But are laypeople's judgments reliable indicators of quality, or are they corrupted by either partisan bias or lack of information? Perhaps surprisingly, we find that laypeople—on average—are quite good at distinguishing between lower- and higher-quality sources. These results indicate that incorporating the trust ratings of laypeople into social media ranking algorithms may prove an effective intervention against misinformation, fake news, and news content with heavy political bias.

With the rise of social media, everyone has the potential to be both a consumer and producer of online content. Although one might assume that people share news because they believe it to be true, worldwide concerns about the spread of misinformation suggest that truthfulness may not be a dominant driver of sharing online. Across three studies (total N=4,786), we investigate what content dimensions are associated with social media sharing intentions for a wide range of news headlines. When we examine the relationships between content dimensions using factor analysis, we consistently find separate factors capturing perceived accuracy and evocativeness. The perceived accuracy factor was positively correlated with both sharing intentions and the headline's objective veracity. However, while the evocativeness factor was also positively correlated with sharing intentions, it was consistently negatively correlated with the headline's objective veracity.

© 2020 Owner/Author. How can social media platforms fight the spread of misinformation? One possibility is to use newsfeed algorithms to downrank content from sources that users rate as untrustworthy. But will laypeople be handicapped by motivated reasoning or lack of expertise, and thus unable to identify misinformation sites? And will they "game" this crowdsourcing mechanism in order to promote content that aligns with their partisan agendas? We conducted a survey experiment in which =984 Americans indicated their trust in numerous news sites. To study the tendency of people to game the system, half of the participants were told their responses would inform social media ranking algorithms. Participants trusted mainstream sources much more than hyper-partisan or fake news sources, and their ratings were highly correlated with professional fact-checker judgments. Critically, informing participants that their responses would influence ranking algorithms did not diminish these results, despite the manipulation increasing the political polarization of trust ratings.

A major focus of current research is understanding why people fall for and share fake news on social media. While much research focuses on understanding the role of personality-level traits for those who share the news, such as partisanship and analytic thinking, characteristics of the articles themselves have not been studied. Across two pre-registered studies, we examined whether character-deprecation headlines – headlines designed to deprecate someone's character, but which have no impact on policy or legislation – increased the likelihood of self-reported sharing on social media. In Study 1 we harvested fake news items from online sources and compared sharing intentions between Republicans and Democrats. Results showed that, compared to Democrats, Republicans had greater intention to share character-deprecation headlines compared to news with policy implications. We then applied these findings experimentally. In Study 2 we developed a set of fake news items that was matched for content across pro-Democratic and pro-Republican headlines and across news focusing on a specific person (e.g., Trump) versus a generic person (e.g., a Republican). We found that, contrary to Study 1, Republicans were no more inclined toward character deprecation than Democrats. However, these findings suggest that while character assassination may be a feature of pro-Republican news, it is not more attractive to Republicans versus Democrats. News with policy implications, whether fake or real, seems consistently more attractive to members of both parties regardless of whether it attempts to deprecate an opponent's character. Thus, character deprecation in fake news may in be in supply, but not in demand.

A major focus of current research is understanding why people fall for and share fake news on social media. While much research focuses on understanding the role of personality-level traits for those who share the news, such as partisanship and analytic thinking, characteristics of the articles themselves have not been studied. Across two pre-registered studies, we examined whether character-deprecation headlines – headlines designed to deprecate someone's character, but which have no impact on policy or legislation – increased the likelihood of self-reported sharing on social media. In Study 1 we harvested fake news items from online sources and compared sharing intentions between Republicans and Democrats. Results showed that, compared to Democrats, Republicans had greater intention to share character-deprecation headlines compared to news with policy implications. We then applied these findings experimentally. In Study 2 we developed a set of fake news items that was matched for content across pro-Democratic and pro-Republican headlines and across news focusing on a specific person (e.g., Trump) versus a generic person (e.g., a Republican). We found that, contrary to Study 1, Republicans were no more inclined toward character deprecation than Democrats. However, these findings suggest that while character assassination may be a feature of pro-Republican news, it is not more attractive to Republicans versus Democrats. News with policy implications, whether fake or real, seems consistently more attractive to members of both parties regardless of whether it attempts to deprecate an opponent's character. Thus, character deprecation in fake news may in be in supply, but not in demand.

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. There is an increasing imperative for psychologists and other behavioral scientists to understand how people behave on social media. However, it is often very difficult to execute experimental research on actual social media platforms, or to link survey responses to online behavior in order to perform correlational analyses. Thus, there is a natural desire to use self-reported behavioral intentions in standard survey studies to gain insight into online behavior. But are such hypothetical responses hopelessly disconnected from actual sharing decisions? Or are online survey samples via sources such as Amazon Mechanical Turk (MTurk) so different from the average social media user that the survey responses of one group give little insight into the on-platform behavior of the other? Here we investigate these issues by examining 67 pieces of political news content. We evaluate whether there is a meaningful relationship between (i) the level of sharing (tweets and retweets) of a given piece of content on Twitter, and (ii) the extent to which individuals (total N = 993) in online surveys on MTurk reported being willing to share that same piece of content. We found that the same news headlines that were more likely to be hypothetically shared on MTurk were also shared more frequently by Twitter users, r = .44. For example, across the observed range of MTurk sharing fractions, a 20 percentage point increase in the fraction of MTurk participants who reported being willing to share a news headline on social media was associated with 10x as many actual shares on Twitter. We also found that the correlation between sharing and various features of the headline was similar using both MTurk and Twitter data. These findings suggest that self-reported sharing intentions collected in online surveys are likely to provide some meaningful insight into what content would actually be shared on social media.