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BACKGROUNDEffective communication is crucial during health crises, and social media has become a prominent platform for public health experts to inform and to engage with the public. At the same time, social media also platforms pseudo-experts who may promote contrarian views. Despite the significance of social media, key elements of communication such as the use of moral or emotional language and messaging strategy, particularly during the COVID-19 pandemic, has not been explored. OBJECTIVEThis study aims to analyze how notable public health experts (PHEs) and pseudo-experts communicated with the public during the COVID-19 pandemic. Our focus is the emotional and moral language they used in their messages across a range of pandemic issues. We also study their engagement with political elites and how the public engaged with PHEs to better understand the impact of these health experts on the public discourse. METHODSWe gathered a dataset of original tweets from 489 PHEs and 356 pseudo- experts on Twitter (now X) from January 2020 to January 2021, as well as replies to the original tweets from the PHEs. We identified the key issues that PHEs and pseudo- experts prioritized. We also determined the emotional and moral language in both the original tweets and the replies. This approach enabled us to characterize key priorities for PHEs and pseudo-experts, as well as differences in messaging strategy between these two groups. We also evaluated the influence of PHE language and strategy on the public response. RESULTSOur analyses revealed that PHEs focus on masking, healthcare, education, and vaccines, whereas pseudo-experts discuss therapeutics and lockdowns more frequently. PHEs typically used positive emotional language across all issues, expressing optimism and joy. Pseudo-experts often utilized negative emotions of pessimism and disgust, while limiting positive emotional language to origins and therapeutics. Along the dimensions of moral language, PHEs and pseudo-experts differ on care versus harm, and authority versus subversion, across different issues. Negative emotional and moral language tends to boost engagement in COVID-19 discussions, across all issues. However, the use of positive language by PHEs increases the use of positive language in the public responses. PHEs act as liberal partisans: they express more positive affect in their posts directed at liberals and more negative affect directed at conservative elites. In contrast, pseudo-experts act as conservative partisans. These results provide nuanced insights into the elements that have polarized the COVID-19 discourse. CONCLUSIONSUnderstanding the nature of the public response to PHE’s messages on social media is essential for refining communication strategies during health crises. Our findings emphasize the need for experts to consider the strategic use of moral and emotional language in their messages to reduce polarization and enhance public trust. 

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. 

Effective response to pandemics requires coordinated adoption of mitigation measures, like masking and quarantines, to curb a virus's spread. However, as the COVID-19 pandemic demonstrated, political divisions can hinder consensus on the appropriate response. To better understand these divisions, our study examines a vast collection of COVID-19-related tweets. We focus on five contentious issues: coronavirus origins, lockdowns, masking, education, and vaccines. We describe a weakly supervised method to identify issue-relevant tweets and employ state-of-the-art computational methods to analyze moral language and infer political ideology. We explore how partisanship and moral language shape conversations about these issues. Our findings reveal ideological differences in issue salience and moral language used by different groups. We find that conservatives use more negatively-valenced moral language than liberals and that political elites use moral rhetoric to a greater extent than non-elites across most issues. Examining the evolution and moralization on divisive issues can provide valuable insights into the dynamics of COVID-19 discussions and assist policymakers in better understanding the emergence of ideological divisions. 

Abstract Preferential attachment, homophily, and their consequences such as scale-free (i.e. power-law) degree distributions, the glass ceiling effect (the unseen, yet unbreakable barrier that keeps minorities and women from rising to the upper rungs of the corporate ladder, regardless of their qualifications or achievements) and perception bias are well-studied in undirected networks. However, such consequences and the factors that lead to their emergence in directed networks (e.g. author–citation graphs, Twitter) are yet to be coherently explained in an intuitive, theoretically tractable manner using a single dynamical model. To this end, we present a theoretical and numerical analysis of the novel Directed Mixed Preferential Attachment model in order to explain the emergence of scale-free degree distributions and the glass ceiling effect in directed networks with two groups (minority and majority). Specifically, we first derive closed-form expressions for the power-law exponents of the in-degree and out-degree distributions of each of the two groups and then compare the derived exponents with each other to obtain useful insights. These insights include answers to questions such as: when does the minority group have an out-degree (or in-degree) distribution with a heavier tail compared to the majority group? what factors cause the tail of the out-degree distribution of a group to be heavier than the tail of its own in-degree distribution? what effect does frequent addition of edges between existing nodes have on the in-degree and out-degree distributions of the majority and minority groups? Answers to these questions shed light on the interplay between structure (i.e. the in-degree and out-degree distributions of the two groups) and dynamics (characterized collectively by the homophily, preferential attachment, group sizes and growth dynamics) of various real-world directed networks. We also provide a novel definition of the glass ceiling faced by a group via the number of individuals with large out-degree (i.e. those with many followers) normalized by the number of individuals with large in-degree (i.e. those who follow many others) and then use it to characterize the conditions that cause the glass ceiling effect to emerge in a directed network. Our analytical results are supported by detailed numerical experiments. The DMPA model and its theoretical and numerical analysis provided in this article are useful for analysing various phenomena on directed networks in fields such as network science and computational social science. 

Social networks are very important carriers of information. For instance, the political leaning of our friends can serve as a proxy to identify our own political preferences. This explanatory power is leveraged in many scenarios ranging from business decision‐ making to scientific research to infer missing attributes using machine learning. How‐ ever, factors affecting the performance and the direction of bias of these algorithms are not well understood. To this end, we systematically study how structural properties of the network and the training sample influence the results of collective classification. Our main findings show that (i) mean classification performance can empirically and analytically be predicted by structural properties such as homophily, class balance, edge density and sample size, (ii) small training samples are enough for heterophilic networks to achieve high and unbiased classification performance, even with imper‐ fect model estimates, (iii) homophilic networks are more prone to bias issues and low performance when group size differences increase, (iv) when sampling budgets are small, partial crawls achieve the most accurate model estimates, and degree sampling achieves the highest overall performance. Our findings help practitioners to better understand and evaluate their results when sampling budgets are small or when no ground‐truth is available.