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  1. ; ; ; ; ( , JMIR Preprints)
    BACKGROUND

    Effective 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.

     OBJECTIVE

    This 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.

     METHODS

    We 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.

     RESULTS

    Our 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.

     CONCLUSIONS

    Understanding 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.

     
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    Free, publicly-accessible full text available July 6, 2025
  2. ; ; ( , arXiv)
    Free, publicly-accessible full text available May 2, 2025
  3. ; ; ; ( , ACM Journal on Computing and Sustainable Societies)

    The COVID-19 pandemic has mainstreamed human mobility data into the public domain, with research focused on understanding the impact of mobility reduction policies as well as on regional COVID-19 case prediction models. Nevertheless, current research on COVID-19 case prediction tends to focus on performance improvements, masking relevant insights about when mobility data does not help, and more importantly, why, so that it can adequately inform local decision making. In this article, we carry out a systematic analysis to reveal the conditions under which human mobility data provides (or not) an enhancement over individual regional COVID-19 case prediction models that do not use mobility as a source of information. Our analysis—focused on U.S. county-based COVID-19 case prediction models—shows that (1) at most, 60% of counties improve their performance after adding mobility data; (2) the performance improvements are modest, with median correlation improvements of approximately 0.13; (3) improvements were lower for counties with higher Black, Hispanic, and other non-White populations as well as low-income and rural populations, pointing to potential bias in the mobility data negatively impacting predictive performance; and (4) different mobility datasets, predictive models, and training approaches bring about diverse performance improvements.

     
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    Free, publicly-accessible full text available December 31, 2024
  4. ; ; ; ; ( , arXivorg)
    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. 
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    Accepted Manuscript Full Text Available
  5. ( , Science reports)
    Online misinformation is believed to have contributed to vaccine hesitancy during the Covid-19 pandemic, highlighting concerns about social media’s destabilizing role in public life. Previous research identified a link between political conservatism and sharing misinformation; however, it is not clear how partisanship affects how much misinformation people see online. As a result, we do not know whether partisanship drives exposure to misinformation or people selectively share misinformation despite being exposed to factual content. To address this question, we study Twitter discussions about the Covid-19 pandemic, classifying users along the political and factual spectrum based on the information sources they share. In addition, we quantify exposure through retweet interactions. We uncover partisan asymmetries in the exposure to misinformation: conservatives are more likely to see and share misinformation, and while users’ connections expose them to ideologically congruent content, the interactions between political and factual dimensions create conditions for the highly polarized users—hardline conservatives and liberals—to amplify misinformation. Overall, however, misinformation receives less attention than factual content and political moderates, the bulk of users in our sample, help filter out misinformation. Identifying the extent of polarization and how political ideology exacerbates misinformation can help public health experts and policy makers improve their messaging. 
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    Accepted Manuscript Full Text Available
  6. ; ; ; ; ( , Health Affairs)
    Full Text Available