Small STEPs toward a big change.

Noa Hourvitz is a graduate of the Science Training Encouraging Peace (STEP) program. The STEP Program funds pairs of Israeli and Palestinian students to study together for the length of their graduate degrees. She writes about the friendship she developed with her STEP partner and how science bridged political barriers.

Scientific misinformation: A perfect storm, missteps, and moving forward.

The spread of scientific misinformation is not new but rather has long posed threats to human health, environmental well-being, and the creation of a sustainable and equitable future. However, with the COVID-19 pandemic, the need to develop strategies to counteract scientific misinformation has taken on an acute urgency. Cell editor Nicole Neuman sat down with Walter Quattrociocchi and Dietram Scheufele to gain insights on how we got here and what does-and does not-work to fight the spread of scientific misinformation. Excerpts from this conversation, edited for clarity and length, are presented below, and the full conversation is available with the article online.

Diseases Have No Borders, Neither Should Research!

In response to recent anti-Chinese sentiment in the US, Sunney Xie uses his own experiences to assert that American ideals should not be replaced by nationalism and populism and that everybody wins in Sino-US scientific collaborations, contrary to what Americans are led to believe: that China is the sole beneficiary.

Going Public in Support of Science.

Scientists are stepping up like never before to support science in the public arena. In big and small ways, scientists are adopting creative ideas to promote science.

Breaking Down Walls.

In five days, there will be an election in the United States with immigration as a signature issue. We ask scientists their experiences as immigrants.

Network of large pedigrees reveals social practices of Avar communities.

From AD 567-568, at the onset of the Avar period, populations from the Eurasian Steppe settled in the Carpathian Basin for approximately 250 years1. Extensive sampling for archaeogenomics (424 individuals) and isotopes, combined with archaeological, anthropological and historical contextualization of four Avar-period cemeteries, allowed for a detailed description of the genomic structure of these communities and their kinship and social practices. We present a set of large pedigrees, reconstructed using ancient DNA, spanning nine generations and comprising around 300 individuals. We uncover a strict patrilineal kinship system, in which patrilocality and female exogamy were the norm and multiple reproductive partnering and levirate unions were common. The absence of consanguinity indicates that this society maintained a detailed memory of ancestry over generations. These kinship practices correspond with previous evidence from historical sources and anthropological research on Eurasian Steppe societies2. Network analyses of identity-by-descent DNA connections suggest that social cohesion between communities was maintained via female exogamy. Finally, despite the absence of major ancestry shifts, the level of resolution of our analyses allowed us to detect genetic discontinuity caused by the replacement of a community at one of the sites. This was paralleled with changes in the archaeological record and was probably a result of local political realignment.

Like-minded sources on Facebook are prevalent but not polarizing.

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 problem1,2. 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.

Users choose to engage with more partisan news than they are exposed to on Google Search.

If popular online platforms systematically expose their users to partisan and unreliable news, they could potentially contribute to societal issues such as rising political polarization1,2. This concern is central to the 'echo chamber'3-5 and 'filter bubble'6,7 debates, which critique the roles that user choice and algorithmic curation play in guiding users to different online information sources8-10. These roles can be measured as exposure, defined as the URLs shown to users by online platforms, and engagement, defined as the URLs selected by users. However, owing to the challenges of obtaining ecologically valid exposure data-what real users were shown during their typical platform use-research in this vein typically relies on engagement data4,8,11-16 or estimates of hypothetical exposure17-23. Studies involving ecological exposure have therefore been rare, and largely limited to social media platforms7,24, leaving open questions about web search engines. To address these gaps, we conducted a two-wave study pairing surveys with ecologically valid measures of both exposure and engagement on Google Search during the 2018 and 2020 US elections. In both waves, we found more identity-congruent and unreliable news sources in participants' engagement choices, both within Google Search and overall, than they were exposed to in their Google Search results. These results indicate that exposure to and engagement with partisan or unreliable news on Google Search are driven not primarily by algorithmic curation but by users' own choices.

Historical narratives about the COVID-19 pandemic are motivationally biased.

How people recall the SARS-CoV-2 pandemic is likely to prove crucial in future societal debates on pandemic preparedness and appropriate political action. Beyond simple forgetting, previous research suggests that recall may be distorted by strong motivations and anchoring perceptions on the current situation1-6. Here, using 4 studies across 11 countries (total n = 10,776), we show that recall of perceived risk, trust in institutions and protective behaviours depended strongly on current evaluations. Although both vaccinated and unvaccinated individuals were affected by this bias, people who identified strongly with their vaccination status-whether vaccinated or unvaccinated-tended to exhibit greater and, notably, opposite distortions of recall. Biased recall was not reduced by providing information about common recall errors or small monetary incentives for accurate recall, but was partially reduced by high incentives. Thus, it seems that motivation and identity influence the direction in which the recall of the past is distorted. Biased recall was further related to the evaluation of past political action and future behavioural intent, including adhering to regulations during a future pandemic or punishing politicians and scientists. Together, the findings indicate that historical narratives about the COVID-19 pandemic are motivationally biased, sustain societal polarization and affect preparation for future pandemics. Consequently, future measures must look beyond immediate public-health implications to the longer-term consequences for societal cohesion and trust.

Discriminatory attitudes against unvaccinated people during the pandemic.

During the COVID-19 pandemic, sizeable groups of unvaccinated people persist even in countries with high vaccine access1. As a consequence, vaccination became a controversial subject of debate and even protest2. Here we assess whether people express discriminatory attitudes in the form of negative affectivity, stereotypes and exclusionary attitudes in family and political settings across groups defined by COVID-19 vaccination status. We quantify discriminatory attitudes between vaccinated and unvaccinated citizens in 21 countries, covering a diverse set of cultures across the world. Across three conjoined experimental studies (n = 15,233), we demonstrate that vaccinated people express discriminatory attitudes towards unvaccinated individuals at a level as high as discriminatory attitudes that are commonly aimed at immigrant and minority populations3-5. By contrast, there is an absence of evidence that unvaccinated individuals display discriminatory attitudes towards vaccinated people, except for the presence of negative affectivity in Germany and the USA. We find evidence in support of discriminatory attitudes against unvaccinated individuals in all countries except for Hungary and Romania, and find that discriminatory attitudes are more strongly expressed in cultures with stronger cooperative norms. Previous research on the psychology of cooperation has shown that individuals react negatively against perceived 'free-riders'6,7, including in the domain of vaccinations8,9. Consistent with this, we find that contributors to the public good of epidemic control (that is, vaccinated individuals) react with discriminatory attitudes towards perceived free-riders (that is, unvaccinated individuals). National leaders and vaccinated members of the public appealed to moral obligations to increase COVID-19 vaccine uptake10,11, but our findings suggest that discriminatory attitudes-including support for the removal of fundamental rights-simultaneously emerged.

Biology boom goes bust.

Political and industrial factors-beyond the economic headwinds-may be causing public investment in biomedical research to slow down around the world.

The obligation for biologists to commit to political advocacy.

I explain here why all scientists should feel obligated to do their part to support the community by advocating for the benefits of government investments in scientific research and training.

Measuring algorithmically infused societies.

It has been the historic responsibility of the social sciences to investigate human societies. Fulfilling this responsibility requires social theories, measurement models and social data. Most existing theories and measurement models in the social sciences were not developed with the deep societal reach of algorithms in mind. The emergence of 'algorithmically infused societies'-societies whose very fabric is co-shaped by algorithmic and human behaviour-raises three key challenges: the insufficient quality of measurements, the complex consequences of (mis)measurements, and the limits of existing social theories. Here we argue that tackling these challenges requires new social theories that account for the impact of algorithmic systems on social realities. To develop such theories, we need new methodologies for integrating data and measurements into theory construction. Given the scale at which measurements can be applied, we believe measurement models should be trustworthy, auditable and just. To achieve this, the development of measurements should be transparent and participatory, and include mechanisms to ensure measurement quality and identify possible harms. We argue that computational social scientists should rethink what aspects of algorithmically infused societies should be measured, how they should be measured, and the consequences of doing so.

Quantifying social organization and political polarization in online platforms.

Mass selection into groups of like-minded individuals may be fragmenting and polarizing online society, particularly with respect to partisan differences1-4. However, our ability to measure the social makeup of online communities and in turn, to understand the social organization of online platforms, is limited by the pseudonymous, unstructured and large-scale nature of digital discussion. Here we develop a neural-embedding methodology to quantify the positioning of online communities along social dimensions by leveraging large-scale patterns of aggregate behaviour. Applying our methodology to 5.1 billion comments made in 10,000 communities over 14 years on Reddit, we measure how the macroscale community structure is organized with respect to age, gender and US political partisanship. Examining political content, we find that Reddit underwent a significant polarization event around the 2016 US presidential election. Contrary to conventional wisdom, however, individual-level polarization is rare; the system-level shift in 2016 was disproportionately driven by the arrival of new users. Political polarization on Reddit is unrelated to previous activity on the platform and is instead temporally aligned with external events. We also observe a stark ideological asymmetry, with the sharp increase in polarization in 2016 being entirely attributable to changes in right-wing activity. This methodology is broadly applicable to the study of online interaction, and our findings have implications for the design of online platforms, understanding the social contexts of online behaviour, and quantifying the dynamics and mechanisms of online polarization.

Shifting attention to accuracy can reduce misinformation online.

In recent years, there has been a great deal of concern about the proliferation of false and misleading news on social media1-4. Academics and practitioners alike have asked why people share such misinformation, and sought solutions to reduce the sharing of misinformation5-7. Here, we attempt to address both of these questions. First, we find that the veracity of headlines has little effect on sharing intentions, despite having a large effect on judgments of accuracy. This dissociation suggests that sharing does not necessarily indicate belief. Nonetheless, most participants say it is important to share only accurate news. To shed light on this apparent contradiction, we carried out four survey experiments and a field experiment on Twitter; the results show that subtly shifting attention to accuracy increases the quality of news that people subsequently share. Together with additional computational analyses, these findings indicate that people often share misinformation because their attention is focused on factors other than accuracy-and therefore they fail to implement a strongly held preference for accurate sharing. Our results challenge the popular claim that people value partisanship over accuracy8,9, and provide evidence for scalable attention-based interventions that social media platforms could easily implement to counter misinformation online.

Towards a rigorous understanding of societal responses to climate change.

A large scholarship currently holds that before the onset of anthropogenic global warming, natural climatic changes long provoked subsistence crises and, occasionally, civilizational collapses among human societies. This scholarship, which we term the 'history of climate and society' (HCS), is pursued by researchers from a wide range of disciplines, including archaeologists, economists, geneticists, geographers, historians, linguists and palaeoclimatologists. We argue that, despite the wide interest in HCS, the field suffers from numerous biases, and often does not account for the local effects and spatiotemporal heterogeneity of past climate changes or the challenges of interpreting historical sources. Here we propose an interdisciplinary framework for uncovering climate-society interactions that emphasizes the mechanics by which climate change has influenced human history, and the uncertainties inherent in discerning that influence across different spatiotemporal scales. Although we acknowledge that climate change has sometimes had destructive effects on past societies, the application of our framework to numerous case studies uncovers five pathways by which populations survived-and often thrived-in the face of climatic pressures.

January 6 arrests and media coverage do not remobilize conservatives on social media.

Social media's pivotal role in catalyzing social movements is widely acknowledged across scientific disciplines. Past research has predominantly explored social media's ability to instigate initial mobilization while leaving the question of its capacity to sustain these movements relatively uncharted. This study investigates the persistence of movement activity on Twitter and Gab following a substantial on-the-ground mobilization event catalyzed by social media-the StoptheSteal movement culminating in the January 6th Capitol attack. Our findings indicate that the online communities active in the January 6 mobilization did not display substantial remobilization in the subsequent year. These results highlight the fact that further exploration is needed to understand the factors shaping how and when movements are sustained by social media. In this regard, our study provides valuable insights for scientists across diverse disciplines, on how certain social media platforms may contribute to the evolving dynamics of collective action.

Uncommon and nonpartisan: Antidemocratic attitudes in the American public.

Democratic regimes flourish only when there is broad acceptance of an extensive set of norms and values. In the United States, fundamental democratic norms have recently come under threat from prominent Republican officials. We investigate whether this antidemocratic posture has spread from the elite level to rank-and-file partisans. Exploiting data from a massive repeated cross-sectional and panel survey ([Formula: see text] = 45,095 and 5,231 respectively), we find that overwhelming majorities of the public oppose violations of democratic norms, and virtually nobody supports partisan violence. This bipartisan consensus remains unchanged over time despite high levels of affective polarization and exposure to divisive elite rhetoric during the 2022 political campaign. Additionally, we find no evidence that elected officials' practice of election denialism encourages their constituents to express antidemocratic attitudes. Overall, these results suggest that the clear and present threat to American democracy comes from unilateral actions by political elites that stand in contrast to the views of their constituents. In closing, we consider the implications of the stark disconnect between the behavior of Republican elites and the attitudes of Republican voters.

Evaluating the persuasive influence of political microtargeting with large language models.

Recent advancements in large language models (LLMs) have raised the prospect of scalable, automated, and fine-grained political microtargeting on a scale previously unseen; however, the persuasive influence of microtargeting with LLMs remains unclear. Here, we build a custom web application capable of integrating self-reported demographic and political data into GPT-4 prompts in real-time, facilitating the live creation of unique messages tailored to persuade individual users on four political issues. We then deploy this application in a preregistered randomized control experiment (n = 8,587) to investigate the extent to which access to individual-level data increases the persuasive influence of GPT-4. Our approach yields two key findings. First, messages generated by GPT-4 were broadly persuasive, in some cases increasing support for an issue stance by up to 12 percentage points. Second, in aggregate, the persuasive impact of microtargeted messages was not statistically different from that of non-microtargeted messages (4.83 vs. 6.20 percentage points, respectively, P = 0.226). These trends hold even when manipulating the type and number of attributes used to tailor the message. These findings suggest-contrary to widespread speculation-that the influence of current LLMs may reside not in their ability to tailor messages to individuals but rather in the persuasiveness of their generic, nontargeted messages. We release our experimental dataset, GPTarget2024, as an empirical baseline for future research.