Blocking of counter-partisan accounts drives political assortment on Twitter.

There is strong political assortment of Americans on social media networks. This is typically attributed to preferential tie formation (i.e. homophily) among those with shared partisanship. Here, we demonstrate an additional factor beyond homophily driving assorted networks: preferential prevention of social ties. In two field experiments on Twitter, we created human-looking bot accounts that identified as Democrats or Republicans, and then randomly assigned users to be followed by one of these accounts. In addition to preferentially following-back copartisans, we found that users were 12 times more likely to block counter-partisan accounts compared to copartisan accounts in the first experiment, and 4 times more likely to block counter-partisan accounts relative to a neutral account or a copartisan account in the second experiment. We then replicated these findings in a survey experiment and found evidence of a key motivation for blocking: wanting to avoid seeing any content posted by the blocked user. Additionally, we found that Democrats preferentially blocked counter-partisans more than Republicans, and that this asymmetry was likely due to blocking accounts who post low-quality or politically slanted content (rather than an asymmetry in identity-based blocking). Our results demonstrate that preferential blocking of counter-partisans is an important phenomenon driving political assortment on social media.

Toolbox of individual-level interventions against online misinformation.

The spread of misinformation through media and social networks threatens many aspects of society, including public health and the state of democracies. One approach to mitigating the effect of misinformation focuses on individual-level interventions, equipping policymakers and the public with essential tools to curb the spread and influence of falsehoods. Here we introduce a toolbox of individual-level interventions for reducing harm from online misinformation. Comprising an up-to-date account of interventions featured in 81 scientific papers from across the globe, the toolbox provides both a conceptual overview of nine main types of interventions, including their target, scope and examples, and a summary of the empirical evidence supporting the interventions, including the methods and experimental paradigms used to test them. The nine types of interventions covered are accuracy prompts, debunking and rebuttals, friction, inoculation, lateral reading and verification strategies, media-literacy tips, social norms, source-credibility labels, and warning and fact-checking labels.

Misinformation and harmful language are interconnected, rather than distinct, challenges.

There is considerable concern about users posting misinformation and harmful language on social media. Substantial-yet largely distinct-bodies of research have studied these two kinds of problematic content. Here, we shed light on both research streams by examining the relationship between the sharing of misinformation and the use of harmful language. We do so by creating and analyzing a dataset of 8,687,758 posts from N = 6,832 Twitter (now called X) users, and a dataset of N = 14,617 true and false headlines from professional fact-checking websites. Our analyses reveal substantial positive associations between misinformation and harmful language. On average, Twitter posts containing links to lower-quality news outlets also contain more harmful language (ß = 0.10); and false headlines contain more harmful language than true headlines (ß = 0.19). Additionally, Twitter users who share links to lower-quality news sources also use more harmful language-even in non-news posts that are unrelated to (mis)information (ß = 0.13). These consistent findings across different datasets and levels of analysis suggest that misinformation and harmful language are related in important ways, rather than being distinct phenomena. At the same, however, the strength of associations is not sufficiently high to make the presence of harmful language a useful diagnostic for information quality: most low-quality information does not contain harmful language, and a considerable fraction of high-quality information does contain harmful language. Overall, our results underscore important opportunities to integrate these largely disconnected strands of research and understand their psychological connections.

On the Efficacy of Accuracy Prompts Across Partisan Lines: An Adversarial Collaboration.

The spread of misinformation is a pressing societal challenge. Prior work shows that shifting attention to accuracy increases the quality of people's news-sharing decisions. However, researchers disagree on whether accuracy-prompt interventions work for U.S. Republicans/conservatives and whether partisanship moderates the effect. In this preregistered adversarial collaboration, we tested this question using a multiverse meta-analysis (k = 21; N = 27,828). In all 70 models, accuracy prompts improved sharing discernment among Republicans/conservatives. We observed significant partisan moderation for single-headline "evaluation" treatments (a critical test for one research team) such that the effect was stronger among Democrats than Republicans. However, this moderation was not consistently robust across different operationalizations of ideology/partisanship, exclusion criteria, or treatment type. Overall, we observed significant partisan moderation in 50% of specifications (all of which were considered critical for the other team). We discuss the conditions under which moderation is observed and offer interpretations.

The distorting effects of producer strategies: Why engagement does not reveal consumer preferences for misinformation.

A great deal of empirical research has examined who falls for misinformation and why. Here, we introduce a formal game-theoretic model of engagement with news stories that captures the strategic interplay between (mis)information consumers and producers. A key insight from the model is that observed patterns of engagement do not necessarily reflect the preferences of consumers. This is because producers seeking to promote misinformation can use strategies that lead moderately inattentive readers to engage more with false stories than true ones-even when readers prefer more accurate over less accurate information. We then empirically test people's preferences for accuracy in the news. In three studies, we find that people strongly prefer to click and share news they perceive as more accurate-both in a general population sample, and in a sample of users recruited through Twitter who had actually shared links to misinformation sites online. Despite this preference for accurate news-and consistent with the predictions of our model-we find markedly different engagement patterns for articles from misinformation versus mainstream news sites. Using 1,000 headlines from 20 misinformation and 20 mainstream news sites, we compare Facebook engagement data with 20,000 accuracy ratings collected in a survey experiment. Engagement with a headline is negatively correlated with perceived accuracy for misinformation sites, but positively correlated with perceived accuracy for mainstream sites. Taken together, these theoretical and empirical results suggest that consumer preferences cannot be straightforwardly inferred from empirical patterns of engagement.

TimeTeller: A tool to probe the circadian clock as a multigene dynamical system.

Recent studies have established that the circadian clock influences onset, progression and therapeutic outcomes in a number of diseases including cancer and heart diseases. Therefore, there is a need for tools to measure the functional state of the molecular circadian clock and its downstream targets in patients. Moreover, the clock is a multi-dimensional stochastic oscillator and there are few tools for analysing it as a noisy multigene dynamical system. In this paper we consider the methodology behind TimeTeller, a machine learning tool that analyses the clock as a noisy multigene dynamical system and aims to estimate circadian clock function from a single transcriptome by modelling the multi-dimensional state of the clock. We demonstrate its potential for clock systems assessment by applying it to mouse, baboon and human microarray and RNA-seq data and show how to visualise and quantify the global structure of the clock, quantitatively stratify individual transcriptomic samples by clock dysfunction and globally compare clocks across individuals, conditions and tissues thus highlighting its potential relevance for advancing circadian medicine.

Academics are more specific, and practitioners more sensitive, in forecasting interventions to strengthen democratic attitudes.

Concern over democratic erosion has led to a proliferation of proposed interventions to strengthen democratic attitudes in the United States. Resource constraints, however, prevent implementing all proposed interventions. One approach to identify promising interventions entails leveraging domain experts, who have knowledge regarding a given field, to forecast the effectiveness of candidate interventions. We recruit experts who develop general knowledge about a social problem (academics), experts who directly intervene on the problem (practitioners), and nonexperts from the public to forecast the effectiveness of interventions to reduce partisan animosity, support for undemocratic practices, and support for partisan violence. Comparing 14,076 forecasts submitted by 1,181 forecasters against the results of a megaexperiment (n = 32,059) that tested 75 hypothesized effects of interventions, we find that both types of experts outperformed members of the public, though experts differed in how they were accurate. While academics' predictions were more specific (i.e., they identified a larger proportion of ineffective interventions and had fewer false-positive forecasts), practitioners' predictions were more sensitive (i.e., they identified a larger proportion of effective interventions and had fewer false-negative forecasts). Consistent with this, practitioners were better at predicting best-performing interventions, while academics were superior in predicting which interventions performed worst. Our paper highlights the importance of differentiating types of experts and types of accuracy. We conclude by discussing factors that affect whether sensitive or specific forecasters are preferable, such as the relative cost of false positives and negatives and the expected rate of intervention success.

A synthesis of evidence for policy from behavioural science during COVID-19.

Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations ('claims') detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms 'physical distancing' and 'social distancing'. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.

Crowds Can Effectively Identify Misinformation at Scale.

Identifying successful approaches for reducing the belief and spread of online misinformation is of great importance. Social media companies currently rely largely on professional fact-checking as their primary mechanism for identifying falsehoods. However, professional fact-checking has notable limitations regarding coverage and speed. In this article, we summarize research suggesting that the "wisdom of crowds" can be harnessed successfully to help identify misinformation at scale. Despite potential concerns about the abilities of laypeople to assess information quality, recent evidence demonstrates that aggregating judgments of groups of laypeople, or crowds, can effectively identify low-quality news sources and inaccurate news posts: Crowd ratings are strongly correlated with fact-checker ratings across a variety of studies using different designs, stimulus sets, and subject pools. We connect these experimental findings with recent attempts to deploy crowdsourced fact-checking in the field, and we close with recommendations and future directions for translating crowdsourced ratings into effective interventions.

Misinformation warning labels are widely effective: A review of warning effects and their moderating features.

There is growing concern over the spread of misinformation online. One widely adopted intervention by platforms for addressing falsehoods is applying "warning labels" to posts deemed inaccurate by fact-checkers. Despite a rich literature on correcting misinformation after exposure, much less work has examined the effectiveness of warning labels presented concurrent with exposure. Promisingly, existing research suggests that warning labels effectively reduce belief and spread of misinformation. The size of these beneficial effects depends on how the labels are implemented and the characteristics of the content being labeled. Despite some individual differences, recent evidence indicates that warning labels are generally effective across party lines and other demographic characteristics. We discuss potential implications and limitations of labeling policies for addressing online misinformation.

High level of correspondence across different news domain quality rating sets.

One widely used approach for quantifying misinformation consumption and sharing is to evaluate the quality of the news domains that a user interacts with. However, different media organizations and fact-checkers have produced different sets of news domain quality ratings, raising questions about the reliability of these ratings. In this study, we compared six sets of expert ratings and found that they generally correlated highly with one another. We then created a comprehensive set of domain ratings for use by the research community (github.com/hauselin/domain-quality-ratings), leveraging an ensemble "wisdom of experts" approach. To do so, we performed imputation together with principal component analysis to generate a set of aggregate ratings. The resulting rating set comprises 11,520 domains-the most extensive coverage to date-and correlates well with other rating sets that have more limited coverage. Together, these results suggest that experts generally agree on the relative quality of news domains, and the aggregate ratings that we generate offer a powerful research tool for evaluating the quality of news consumed or shared and the efficacy of misinformation interventions.

Conscientiousness does not moderate the association between political ideology and susceptibility to fake news sharing.

Recent work suggests that personality moderates the relationship between political ideology and the sharing of misinformation. Specifically, Lawson and Kakkar (2022) claimed that fake news sharing was driven mostly by low conscientiousness conservatives. We reanalyzed their data and conducted five new preregistered conceptual replications to reexamine their claims (N = 2,433; stopping rule determined via Bayesian sequential sampling). The results did not support their claim that conscientious conservatives shared less fake news; instead, their findings pertain to overall sharing rates (of both true and fake news), rather than specifically to fake news. That is, the association between conscientiousness and misinformation sharing (when it occurs) is explained by lower overall sharing instead of a particular resistance to fake news per se. Our results highlight the importance of distinguishing between overall sharing tendencies and the sharing of misinformation specifically, which have different theoretical and practical implications for how to combat the spread of misinformation. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Parasite manipulation of host phenotypes inferred from transcriptional analyses in a trematode-amphipod system.

Manipulation of host phenotypes by parasites is hypothesized to be an adaptive strategy enhancing parasite transmission across hosts and generations. Characterizing the molecular mechanisms of manipulation is important to advance our understanding of host-parasite coevolution. The trematode (Levinseniella byrdi) is known to alter the colour and behaviour of its amphipod host (Orchestia grillus) presumably increasing predation of amphipods which enhances trematode transmission through its life cycle. We sampled 24 infected and 24 uninfected amphipods from a salt marsh in Massachusetts to perform differential gene expression analysis. In addition, we constructed novel genomic tools for O. grillus including a de novo genome and transcriptome. We discovered that trematode infection results in upregulation of amphipod transcripts associated with pigmentation and detection of external stimuli, and downregulation of multiple amphipod transcripts implicated in invertebrate immune responses, such as vacuolar ATPase genes. We hypothesize that suppression of immune genes and the altered expression of genes associated with coloration and behaviour may allow the trematode to persist in the amphipod and engage in further biochemical manipulation that promotes transmission. The genomic tools and transcriptomic analyses reported provide new opportunities to discover how parasites alter diverse pathways underlying host phenotypic changes in natural populations.

No association between numerical ability and politically motivated reasoning in a large US probability sample.

The highly influential theory of "Motivated System 2 Reasoning" argues that analytical, deliberative ("System 2") reasoning is hijacked by identity when considering ideologically charged issues-leading people who are more likely to engage in such reasoning to be more polarized, rather than more accurate. Here, we fail to replicate the key empirical support for this theory across five contentious issues, using a large gold-standard nationally representative probability sample of Americans. While participants were more accurate in evaluating a contingency table when the outcome aligned with their politics (even when controlling for prior beliefs), we find that participants with higher numeracy were more accurate in evaluating the contingency table, regardless of whether or not the table's outcome aligned with their politics. These findings call for a reconsideration of the effect of identity on analytical reasoning.

Quantifying the potential persuasive returns to political microtargeting.

Much concern has been raised about the power of political microtargeting to sway voters' opinions, influence elections, and undermine democracy. Yet little research has directly estimated the persuasive advantage of microtargeting over alternative campaign strategies. Here, we do so using two studies focused on U.S. policy issue advertising. To implement a microtargeting strategy, we combined machine learning with message pretesting to determine which advertisements to show to which individuals to maximize persuasive impact. Using survey experiments, we then compared the performance of this microtargeting strategy against two other messaging strategies. Overall, we estimate that our microtargeting strategy outperformed these strategies by an average of 70% or more in a context where all of the messages aimed to influence the same policy attitude (Study 1). Notably, however, we found no evidence that targeting messages by more than one covariate yielded additional persuasive gains, and the performance advantage of microtargeting was primarily visible for one of the two policy issues under study. Moreover, when microtargeting was used instead to identify which policy attitudes to target with messaging (Study 2), its advantage was more limited. Taken together, these results suggest that the use of microtargeting-combining message pretesting with machine learning-can potentially increase campaigns' persuasive influence and may not require the collection of vast amounts of personal data to uncover complex interactions between audience characteristics and political messaging. However, the extent to which this approach confers a persuasive advantage over alternative strategies likely depends heavily on context.

Understanding and combatting misinformation across 16 countries on six continents.

The spread of misinformation online is a global problem that requires global solutions. To that end, we conducted an experiment in 16 countries across 6 continents (N = 34,286; 676,605 observations) to investigate predictors of susceptibility to misinformation about COVID-19, and interventions to combat the spread of this misinformation. In every country, participants with a more analytic cognitive style and stronger accuracy-related motivations were better at discerning truth from falsehood; valuing democracy was also associated with greater truth discernment, whereas endorsement of individual responsibility over government support was negatively associated with truth discernment in most countries. Subtly prompting people to think about accuracy had a generally positive effect on the veracity of news that people were willing to share across countries, as did minimal digital literacy tips. Finally, aggregating the ratings of our non-expert participants was able to differentiate true from false headlines with high accuracy in all countries via the 'wisdom of crowds'. The consistent patterns we observe suggest that the psychological factors underlying the misinformation challenge are similar across different regional settings, and that similar solutions may be broadly effective.

Strain-dependent lung transcriptomic differences in cigarette smoke and LPS models of lung injury in mice.

Cigarette smoking increases the risk of acute respiratory distress syndrome (ARDS; Calfee CS, Matthay MA, Eisner MD, Benowitz N, Call M, Pittet J-F, Cohen MJ. Am J Respir Crit Care Med 183: 1660-1665, 2011; Calfee CS, Matthay MA, Kangelaris KN, Siew ED, Janz DR, Bernard GR, May AK, Jacob P, Havel C, Benowitz NL, Ware LB. Crit Care Med 43: 1790-1797, 2015; Toy P, Gajic O, Bacchetti P, Looney MR, Gropper MA, Hubmayr R, Lowell CA, Norris PJ, Murphy EL, Weiskopf RB, Wilson G, Koenigsberg M, Lee D, Schuller R, Wu P, Grimes B, Gandhi MJ, Winters JL, Mair D, Hirschler N, Sanchez Rosen R, Matthay MA, TRALI Study Group. Blood 119: 1757-1767, 2012) and causes emphysema. However, it is not known why some individuals develop disease, whereas others do not. We found that smoke-exposed AKR mice were more susceptible to lipopolysaccharides (LPS)-induced acute lung injury (ALI) than C57BL/6 mice (Sakhatskyy P, Wang Z, Borgas D, Lomas-Neira J, Chen Y, Ayala A, Rounds S, Lu Q. Am J Physiol Lung Cell Mol Physiol 312: L56-L67, 2017); thus, we investigated strain-dependent lung transcriptomic responses to cigarette smoke (CS). Eight-week-old male AKR and C57BL/6 mice were exposed to 3 wk of room air (RA) or cigarette smoke (CS) for 6 h/day, 4 days/wk, followed by intratracheal instillation of LPS or normal saline (NS) and microarray analysis of lung homogenate gene expression. Other groups of AKR and C57 mice were exposed to RA or CS for 6 wk, followed by evaluation of static lung compliance and tissue elastance, morphometric evaluation for emphysema, or microarray analysis of lung gene expression. Transcriptomic analyses of lung homogenates show distinct strain-dependent lung transcriptional responses to CS and LPS, with AKR mice having larger numbers of genes affected than similarly treated C57 mice, congruent with strain differences in physiologic and inflammatory parameters previously observed in LPS-induced ALI after CS priming. These results suggest that genetic differences may underlie differing susceptibility of smokers to ARDS and emphysema. Strain-based differences in gene transcription contribute to CS and LPS-induced lung injury. There may be a genetic basis for smoking-related lung injury. Clinicians should consider cigarette smoke exposure as a risk factor for ALI and ARDS.NEW & NOTEWORTHY We demonstrate that transcriptomes expressed in lung homogenates also differ between the mouse strains and after acute (3 wk) exposure of animals to cigarette smoke (CS) and/or to lipopolysaccharide. Mouse strains also differed in physiologic, pathologic, and transcriptomic, responses to more prolonged (6 wk) exposure to CS. These data support a genetic basis for enhanced susceptibility to acute and chronic lung injury among humans who smoke cigarettes.

Correcting misperceptions of out-partisans decreases American legislators' support for undemocratic practices.

There is substantial concern about democratic backsliding in the United States. Evidence includes notably high levels of animosity toward out-partisans and support for undemocratic practices (SUP) among the general public. Much less is known, however, about the views of elected officials-even though they influence democratic outcomes more directly. In a survey experiment conducted with state legislators (N = 534), we show that these officials exhibit less animosity toward the other party, less SUP, and less support for partisan violence (SPV) than the general public. However, legislators vastly overestimate the levels of animosity, SUP, and SPV among voters from the other party (though not among voters from their own party). Further, those legislators randomly assigned to receive accurate information about the views of voters from the other party reported significantly lower SUP and marginally significantly lower partisan animosity toward the other party. This suggests that legislators' democratic attitudes are causally linked to their perceptions of other-party voters' democratic attitudes. Our findings highlight the importance of ensuring that office holders have access to reliable information about voters from both parties.