Public health requires collective action—the public best addresses health crises when individuals engage in prosocial behaviors. Failure to do so can have dire societal and economic consequences. This was made clear by the disjointed, politicized response to COVID-19 in the United States. Perhaps no aspect of the pandemic exemplified this challenge more than the sizeable percentage of individuals who delayed or refused vaccination. While scholars, practitioners, and the government devised a range of communication strategies to persuade people to get vaccinated, much less attention has been paid to where the unvaccinated could be reached. We address this question using multiple waves of a large national survey as well as various secondary data sets. We find that the vaccine resistant seems to predictably obtain information from conservative media outlets (e.g. Fox News) while the vaccinated congregate around more liberal outlets (e.g. MSNBC). We also find consistent evidence that vaccine-resistant individuals often obtain COVID-19 information from various social media, most notably Facebook, rather than traditional media sources. Importantly, such individuals tend to exhibit low institutional trust. While our results do not suggest a failure of sites such as Facebook's institutional COVID-19 efforts, as the counterfactual of no efforts is unknown, they do highlight an opportunity to reach those who are less likely to take vital actions in the service of public health.
Understanding Local News Social Coverage and Engagement at Scale during the COVID-19 Pandemic
During the COVID-19 pandemic, local news organizations have played an important role in keeping communities informed about the spread and impact of the virus. We explore how political, social media, and economic factors impacted the way local media reported on COVID-19 developments at a national scale between January 2020 and July 2021. We construct and make available a dataset of over 10,000 local news organizations and their social media handles across the U.S. We use social media data to estimate the population reach of outlets (their “localness”), and capture underlying content relationships between them. Building on this data, we analyze how local and national media covered four key COVID-19 news topics: Statistics and Case Counts, Vaccines and Testing, Public Health Guidelines, and Economic Effects. Our results show that news outlets with higher population reach reported proportionally more on COVID-19 than more local outlets. Separating the analysis by topic, we expose more nuanced trends, for example that outlets with a smaller population reach covered the Statistics and Case Counts topic proportionally more, and the Economic Effects topic proportionally less. Our analysis further shows that people engaged proportionally more and used stronger reactions when COVID-19 news were posted by outlets with a smaller population reach. Finally, we demonstrate that COVID-19 posts in Republican-leaning counties generally received more comments and fewer likes than in Democratic counties, perhaps indicating controversy.
more »
« less
- Award ID(s):
- 1840751
- NSF-PAR ID:
- 10379039
- Date Published:
- Journal Name:
- Proceedings of the International AAAI Conference on Web and Social Media
- Volume:
- 16
- ISSN:
- 2162-3449
- Page Range / eLocation ID:
- 560 to 572
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
Mutz, Diana (Ed.)more » « less
Abstract -
This study analyzes and compares how the digital semantic infrastructure of U.S. based digital news varies according to certain characteristics of the media outlet, including the community it serves, the content management system (CMS) it uses, and its institutional affiliation (or lack thereof). Through a multi-stage analysis of the actual markup found on news outlets’ online text articles, we reveal how multiple factors may be limiting the discoverability and reach of online media organizations focused on serving specific communities. Conceptually, we identify markup and metadata as aspects of the semantic infrastructure underpinning platforms’ mechanisms of distributing online news. Given the significant role that these platforms play in shaping the broader visibility of news content, we further contend that this markup therefore constitutes a kind of infrastructure of visibility by which news sources and voices are rendered accessible—or, conversely—invisible in the wider platform economy of journalism. We accomplish our analysis by first identifying key forms of digital markup whose structured data is designed to make online news articles more readily discoverable by search engines and social media platforms. We then analyze 2,226 digital news stories gathered from the main pages of 742 national, local, Black, and other identity-based news organizations in mid-2021, and analyze each for the presence of specific tags reflecting the Schema.org, OpenGraph, and Twitter metadata structures. We then evaluate the relationship between audience focus and the robustness of this digital semantic infrastructure. While we find only a weak relationship between the markup and the community served, additional analysis revealed a much stronger association between these metadata tags and content management system (CMS), in which 80% of the attributes appearing on an article were the same for a given CMS, regardless of publisher, market, or audience focus. Based on this finding, we identify the organizational characteristics that may influence the specific CMS used for digital publishing, and, therefore, the robustness of the digital semantic infrastructure deployed by the organization. Finally, we reflect on the potential implications of the highly disparate tag use we observe, particularly with respect to the broader visibility of online news designed to serve particular US communities.more » « less
-
Risk perception and risk averting behaviors of public agencies in the emergence and spread of COVID-19 can be retrieved through online social media (Twitter), and such interactions can be echoed in other information outlets. This study collected time-sensitive online social media data and analyzed patterns of health risk communication of public health and emergency agencies in the emergence and spread of novel coronavirus using data-driven methods. The major focus is toward understanding how policy-making agencies communicate risk and response information through social media during a pandemic and influence community response—ie, timing of lockdown, timing of reopening, etc.—and disease outbreak indicators—ie, number of confirmed cases and number of deaths. Twitter data of six major public organizations (1,000-4,500 tweets per organization) are collected from February 21, 2020 to June 6, 2020. Several machine learning algorithms, including dynamic topic model and sentiment analysis, are applied over time to identify the topic dynamics over the specific timeline of the pandemic. Organizations emphasized on various topics—eg, importance of wearing face mask, home quarantine, understanding the symptoms, social distancing and contact tracing, emerging community transmission, lack of personal protective equipment, COVID-19 testing and medical supplies, effect of tobacco, pandemic stress management, increasing hospitalization rate, upcoming hurricane season, use of convalescent plasma for COVID-19 treatment, maintaining hygiene, and the role of healthcare podcast in different timeline. The findings can benefit emergency management, policymakers, and public health agencies to identify targeted information dissemination policies for public with diverse needs based on how local, federal, and international agencies reacted to COVID-19.more » « less
-
null (Ed.)Abstract The objective of this study is to examine the transmission risk of COVID-19 based on cross-county population co-location data from Facebook. The rapid spread of COVID-19 in the United States has imposed a major threat to public health, the real economy, and human well-being. With the absence of effective vaccines, the preventive actions of social distancing, travel reduction and stay-at-home orders are recognized as essential non-pharmacologic approaches to control the infection and spatial spread of COVID-19. Prior studies demonstrated that human movement and mobility drove the spatiotemporal distribution of COVID-19 in China. Little is known, however, about the patterns and effects of co-location reduction on cross-county transmission risk of COVID-19. This study utilizes Facebook co-location data for all counties in the United States from March to early May 2020 for conducting spatial network analysis where nodes represent counties and edge weights are associated with the co-location probability of populations of the counties. The analysis examines the synchronicity and time lag between travel reduction and pandemic growth trajectory to evaluate the efficacy of social distancing in ceasing the population co-location probabilities, and subsequently the growth in weekly new cases across counties. The results show that the mitigation effects of co-location reduction appear in the growth of weekly new confirmed cases with one week of delay. The analysis categorizes counties based on the number of confirmed COVID-19 cases and examines co-location patterns within and across groups. Significant segregation is found among different county groups. The results suggest that within-group co-location probabilities (e.g., co-location probabilities among counties with high numbers of cases) remain stable, and social distancing policies primarily resulted in reduced cross-group co-location probabilities (due to travel reduction from counties with large number of cases to counties with low numbers of cases). These findings could have important practical implications for local governments to inform their intervention measures for monitoring and reducing the spread of COVID-19, as well as for adoption in future pandemics. Public policy, economic forecasting, and epidemic modeling need to account for population co-location patterns in evaluating transmission risk of COVID-19 across counties.more » « less
-
null (Ed.)Since the start of coronavirus disease 2019 (COVID-19) pandemic, social media platforms have been filled with discussions about the global health crisis. Meanwhile, the World Health Organization (WHO) has highlighted the importance of seeking credible sources of information on social media regarding COVID-19. In this study, we conducted an in-depth analysis of Twitter posts about COVID-19 during the early days of the COVID-19 pandemic to identify influential sources of COVID-19 information and understand the characteristics of these sources. We identified influential accounts based on an information diffusion network representing the interactions of Twitter users who discussed COVID-19 in the United States over a 24-h period. The network analysis revealed 11 influential accounts that we categorized as: 1) political authorities (elected government officials), 2) news organizations, and 3) personal accounts. Our findings showed that while verified accounts with a large following tended to be the most influential users, smaller personal accounts also emerged as influencers. Our analysis revealed that other users often interacted with influential accounts in response to news about COVID-19 cases and strongly contested political arguments received the most interactions overall. These findings suggest that political polarization was a major factor in COVID-19 information diffusion. We discussed the implications of political polarization on social media for COVID-19 communication.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1609/icwsm.v16i1.19315
