News Frames and News Exposure Predicting Flu Vaccination Uptake: Evidence from U.S. Newspapers, 2011–2018 Using Computational Methods

Xu, Zhan; Ellis, Lauren; Laffidy, Mary

doi:10.1080/10410236.2020.1818958

Cited by 17 publications

(7 citation statements)

References 36 publications

(48 reference statements)

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“…Ever since the early stages of the outbreak, extensive news coverage followed the progress of vaccine developments, while web users engaged in heated discussions about coronavirus vaccines or vaccines in general on various social media platforms such as Facebook, Twitter, and Instagram [2][3][4]. It is crucial to understand media portrayals and public discussions of coronavirus vaccines during the early stages of the outbreak because they influenced policy-making in public health and public perceptions of and attitudes toward vaccination in the later stage [5][6][7][8][9][10][11]. A comprehensive understanding of the public opinion during the initial phase of infectious outbreaks will inform how public health professionals and policymakers make decisions in addressing public concerns in future outbreaks of infectious diseases [12].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Vaccine Tweets During the Early Stage of the COVID-19 Outbreak in the United States: Topic Modeling Analysis

Jiang¹,

Chu²,

Sun³

2021

JMIR Infodemiology

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Background During the early stages of the COVID-19 pandemic, developing safe and effective coronavirus vaccines was considered critical to arresting the spread of the disease. News and social media discussions have extensively covered the issue of coronavirus vaccines, with a mixture of vaccine advocacies, concerns, and oppositions. Objective This study aimed to uncover the emerging themes in Twitter users’ perceptions and attitudes toward vaccines during the early stages of the COVID-19 outbreak. Methods This study employed topic modeling to analyze tweets related to coronavirus vaccines at the start of the COVID-19 outbreak in the United States (February 21 to March 20, 2020). We created a predefined query (eg, “COVID” AND “vaccine”) to extract the tweet text and metadata (number of followers of the Twitter account and engagement metrics based on likes, comments, and retweeting) from the Meltwater database. After preprocessing the data, we tested Latent Dirichlet Allocation models to identify topics associated with these tweets. The model specifying 20 topics provided the best overall coherence, and each topic was interpreted based on its top associated terms. Results In total, we analyzed 100,209 tweets containing keywords related to coronavirus and vaccines. The 20 topics were further collapsed based on shared similarities, thereby generating 7 major themes. Our analysis characterized 26.3% (26,234/100,209) of the tweets as News Related to Coronavirus and Vaccine Development, 25.4% (25,425/100,209) as General Discussion and Seeking of Information on Coronavirus, 12.9% (12,882/100,209) as Financial Concerns, 12.7% (12,696/100,209) as Venting Negative Emotions, 9.9% (9908/100,209) as Prayers and Calls for Positivity, 8.1% (8155/100,209) as Efficacy of Vaccine and Treatment, and 4.9% (4909/100,209) as Conspiracies about Coronavirus and Its Vaccines. Different themes demonstrated some changes over time, mostly in close association with news or events related to vaccine developments. Twitter users who discussed conspiracy theories, the efficacy of vaccines and treatments, and financial concerns had more followers than those focused on other vaccine themes. The engagement level—the extent to which a tweet being retweeted, quoted, liked, or replied by other users—was similar among different themes, but tweets venting negative emotions yielded the lowest engagement. Conclusions This study enriches our understanding of public concerns over new vaccines or vaccine development at early stages of the outbreak, bearing implications for influencing vaccine attitudes and guiding public health efforts to cope with infectious disease outbreaks in the future. This study concluded that public concerns centered on general policy issues related to coronavirus vaccines and that the discussions were considerably mixed with political views when vaccines were not made available. Only a small proportion of tweets focused on conspiracy theories, but these tweets demonstrated high engagement levels and were often contributed by Twitter users with more influence.

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Section: Introductionmentioning

confidence: 99%

Characterization of Vaccine Tweets During the Early Stage of the COVID-19 Outbreak in the United States: Topic Modeling Analysis

Jiang¹,

Chu²,

Sun³

2021

JMIR Infodemiology

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“…We then calculate exposure rate to COVID-19 news based on a state/district's exposure to COVID-19 news and a state's population. 31 Appendix A3 shows the equations.…”

Section: Methodsmentioning

confidence: 99%

“…P i is most recent population in state i . A1 State population data were obtained from the U.S. Census Bureau. A2…”

Section: Appendix A1mentioning

confidence: 99%

“…c k,i is k' s median daily circulation in state i . A1 For example, to calculate a state/district's exposure to COVID-19 news about Asians, E i is the exposure to COVID-19 news articles about Asians in state i . x k is the number of COVID-19 news articles about Asians published in newspaper k .…”

Section: Appendix A1mentioning

confidence: 99%

“…c k,i is k' s median daily circulation in state i . A1 Then, exposure rate to minority news was calculated using the following equation:

1000

where R i is exposure rate to COVID-19 news articles about a racial/ethnic minority group in state i . E i is exposure to COVID-19 news articles about a racial/ethnic minority group in state i .…”

Section: Appendix A1mentioning

confidence: 99%

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Examining U.S. Newspapers’ Effects on COVID-19 Infection Rates Among Racial/Ethnic Minorities

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Health Equity

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Computational Health Communication

Yang¹,

Rains²

2022

The International Encyclopedia of Health Communication

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This entry provides an introduction to computational health communication as an emerging field. We explain five major applications of computational health communication involving opinion mining, predicting group membership, identifying patterns in texts, determining connections between entities, and performing simulations. We also discuss some of the tools employed in these applications, including dictionary‐based text classification, supervised and unsupervised machine learning approaches, social network analysis, and agent‐based modeling. We conclude by pointing out several challenges and pitfalls in computational health communication, and providing suggestions for further research.

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News Frames and News Exposure Predicting Flu Vaccination Uptake: Evidence from U.S. Newspapers, 2011–2018 Using Computational Methods

Cited by 17 publications

References 36 publications

Characterization of Vaccine Tweets During the Early Stage of the COVID-19 Outbreak in the United States: Topic Modeling Analysis

Characterization of Vaccine Tweets During the Early Stage of the COVID-19 Outbreak in the United States: Topic Modeling Analysis

Examining U.S. Newspapers’ Effects on COVID-19 Infection Rates Among Racial/Ethnic Minorities

Computational Health Communication

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