Mask Mandates, Misinformation, and Data Voids in Local News Coverage of COVID-19

Morrow, Garrett; Compagni, Gabriela

doi:10.33774/apsa-2020-f3rnf

Cited by 3 publications

(2 citation statements)

References 32 publications

(34 reference statements)

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“…For example, Thorson et al (2021) focus on many news Facebook pages in the Lansing, Michigan area, while Guo and Sun (2020) perform a content analysis of all the posts from one local news organization. Recent research has started to cover larger samples of local news outlets; exploring trends in local news across 50-100 local sources, analyzing local news owned by larger conglomerates, or focusing on specific states and hand-coding content (Turkel et al 2021;Masullo, Jennings, and Stroud 2021;Morrow and Compagni 2020).…”

Section: Analysis Of Local News Publicationsmentioning

confidence: 99%

“…He et al (2021) model COVID-19 topics and assign partisanship predictions to them through contrasting a CNN and a FOX news corpus, finding that a topic reporting case counts and updates was politically neutral, while the economic COVID-19 news topic was mostly right-leaning. Looking at state-level local outlets, Morrow and Compagni (2020) studied the spread of misinformation, finding an increase in anti-mask stories after the enactment of a state-wide mask mandate. Masullo, Jennings, and Stroud (2021) analyzed what topics around COVID-19 were featured on Facebook posts from local newspapers, and where the information gap exist between local COVID-19 stories provided on social media and public information need.…”

Section: Local News During the Covid-19 Pandemicmentioning

confidence: 99%

See 1 more Smart Citation

Understanding Local News Social Coverage and Engagement at Scale during the COVID-19 Pandemic

Quéré¹,

Chiang²,

Нааман³

2022

ICWSM

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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.

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Section: Analysis Of Local News Publicationsmentioning

confidence: 99%

Section: Local News During the Covid-19 Pandemicmentioning

confidence: 99%

Understanding Local News Social Coverage and Engagement at Scale during the COVID-19 Pandemic

Quéré¹,

Chiang²,

Нааман³

2022

ICWSM

View full text Add to dashboard Cite

show abstract

Uncertainty in the Process of Risk Communication for Public Health: An Integrative Theory-Based Approach

Hong

2024

Palgrave Handbook of Science and Health Journalism

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Social Noise and the Impact of Misinformation on COVID-19 Preventive Measures: Comparative Data Analysis Using Twitter Masking Hashtags

Alsaid

Madali

2022

J. Info. Know. Mgmt.

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The widespread transmission of misinformation regarding the COVID-19 pandemic on social media has become a severe concern for various reasons such as containing the spread of the virus, taking preventive measures, and so on. According to the recent studies, misinformation and conspiracy theories spread on social media have hampered efforts to limit the infection, which has been exacerbated in some instances by politicians and celebrities. Misunderstandings about COVID-19 and wearing a mask sparked much debate. As time went on, a sizable portion of the population continued to refuse to wear masks, owing to extrinsic considerations, such as politics, ideology, personal views, and health concerns. In this study, we look at the concerns surrounding three Twitter hashtags (#masks, #maskup, and #maskoff) in order to understand better how social noise can lead to unintended misinformation. Sentiment analysis, topic modelling, and contextual analysis were used to compare and contrast two datasets relevant to these hashtags, one gathered in 2020 and the other in 2021. According to sentiment analysis, people’s emotions differed between hashtags, and the majority of tweets were based on social media users’ personal opinions. Topic modelling results revealed the prevalence of social noise leading to the unintended spread of misinformation on Twitter. The content analysis results show that while the #maskoff hashtag is used to resist masking influenced by factors, such as misinformation, conspiracy theories, and ideology, the #masks and #maskup hashtags were generally positive and used more to raise awareness of the benefits of wearing masks.

show abstract

Mask Mandates, Misinformation, and Data Voids in Local News Coverage of COVID-19

Cited by 3 publications

References 32 publications

Understanding Local News Social Coverage and Engagement at Scale during the COVID-19 Pandemic

Understanding Local News Social Coverage and Engagement at Scale during the COVID-19 Pandemic

Uncertainty in the Process of Risk Communication for Public Health: An Integrative Theory-Based Approach

Social Noise and the Impact of Misinformation on COVID-19 Preventive Measures: Comparative Data Analysis Using Twitter Masking Hashtags

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