Background: The novel coronavirus pandemic continues to ravage communities across the United States. Opinion surveys identified the importance of political ideology in shaping perceptions of the pandemic and compliance with preventive measures. Objective: The aim of this study was to measure political partisanship and antiscience attitudes in the discussions about the pandemic on social media, as well as their geographic and temporal distributions. Methods: We analyzed a large set of tweets from Twitter related to the pandemic, collected between January and May 2020, and developed methods to classify the ideological alignment of users along the moderacy (hardline vs moderate), political (liberal vs conservative), and science (antiscience vs proscience) dimensions. Results: We found a significant correlation in polarized views along the science and political dimensions. Moreover, politically moderate users were more aligned with proscience views, while hardline users were more aligned with antiscience views. Contrary to expectations, we did not find that polarization grew over time; instead, we saw increasing activity by moderate proscience users. We also show that antiscience conservatives in the United States tended to tweet from the southern and northwestern states, while antiscience moderates tended to tweet from the western states. The proportion of antiscience conservatives was found to correlate with COVID-19 cases. Conclusions: Our findings shed light on the multidimensional nature of polarization and the feasibility of tracking polarized opinions about the pandemic across time and space through social media data.

Numerous studies have reported an increase in hate speech on X (formerly Twitter) in the months immediately following Elon Musk’s acquisition of the platform on October 27th, 2022; relatedly, despite Musk’s pledge to “defeat the spam bots,” a recent study reported no substantial change in the concentration of inauthentic accounts. However, it is not known whether any of these trends endured. We address this by examining material posted on X from the beginning of 2022 through June 2023, the period that includes Musk’s full tenure as CEO. We find that the increase in hate speech just before Musk bought X persisted until at least May of 2023, with the weekly rate of hate speech being approximately 50% higher than the months preceding his purchase, although this increase cannot be directly attributed to any policy at X. The increase is seen across multiple dimensions of hate, including racism, homophobia, and transphobia. Moreover, there is a doubling of hate post “likes,” indicating increased engagement with hate posts. In addition to measuring hate speech, we also measure the presence of inauthentic accounts on the platform; these accounts are often used in spam and malicious information campaigns. We find no reduction (and a possible increase) in activity by these users after Musk purchased X, which could point to further negative outcomes, such as the potential for scams, interference in elections, or harm to public health campaigns. Overall, the long-term increase in hate speech, and the prevalence of potentially inauthentic accounts, are concerning, as these factors can undermine safe and democratic online environments, and increase the risk of offline harms.

Social media platforms, particularly Twitter, have become pivotal arenas for influence campaigns, often orchestrated by state-sponsored information operations (IOs). This paper delves into the detection of key players driving IOs by employing similarity graphs constructed from behavioral pattern data. We unveil that well-known, yet underutilized network properties can help accurately identify coordinated IO drivers. Drawing from a comprehensive dataset of 49 million tweets from six countries, which includes multiple verified IOs, our study reveals that traditional network filtering techniques do not consistently pinpoint IO drivers across campaigns. We first propose a framework based on node pruning that emerges superior, particularly when combining multiple behavioral indicators across different networks. Then, we introduce a supervised machine learning model that harnesses a vector representation of the fused similarity network. This model, which boasts a precision exceeding 0.95, adeptly classifies IO drivers on a global scale and reliably forecasts their temporal engagements. Our findings are crucial in the fight against deceptive influence campaigns on social media, helping us better understand and detect them.

The scaling relations between city attributes and population are emergent and ubiquitous aspects of urban growth. Quantifying these relations and understanding their theoretical foundation, however, is difficult due to the challenge of defining city boundaries and a lack of historical data to study city dynamics over time and space. To address this issue, we analyze scaling between city infrastructure and population across 857 metropolitan areas in the conterminous United States over an unprecedented 115 years (1900–2015) using dasymetrically refined historical population estimates, historical urban road network models, and multi-temporal settlement data to define dynamic city boundaries. We demonstrate that urban scaling exponents closely match theoretical models over a century. Despite some close quantitative agreement with theory, the empirical scaling relations unexpectedly vary across regions. Our analysis of scaling coefficients, meanwhile, reveals that contemporary cities use more developed land and kilometers of road than cities of similar population in 1900, which has serious implications for urban development and impacts on the local environment. Overall, our results provide a new way to study urban systems based on novel, geohistorical data.

The COVID-19 pandemic has posed unprecedented challenges to public health world-wide. To make decisions about mitigation strategies and to understand the disease dynamics, policy makers and epidemiologists must know how the disease is spreading in their communities. Here we analyse confirmed infections and deaths over multiple geographic scales to show that COVID-19’s impact is highly unequal: many regions have nearly zero infections, while others are hot spots. We attribute the effect to a Reed–Hughes-like mechanism in which the disease arrives to regions at different times and grows exponentially at different rates. Faster growing regions correspond to hot spots that dominate spatially aggregated statistics, thereby skewing growth rates at larger spatial scales. Finally, we use these analyses to show that, across multiple spatial scales, the growth rate of COVID-19 has slowed down with each surge. These results demonstrate a trade-off when estimating growth rates: while spatial aggregation lowers noise, it can increase bias. Public policy and epidemic modelling should be aware of, and aim to address, this distortion. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.