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

Jiang, Li; Chu, Tsz Hang; Sun, Mengru

doi:10.2196/25636

Cited by 17 publications

(23 citation statements)

References 69 publications

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“…For example, some studies [22][23][24][25][26] collected tweets over the course of the COVID-19 pandemic to examine public sentiments and opinions regarding COVID-19 vaccines. Researchers [27,28] conducted topic modeling and emotion analyses to identify the themes and emotions related to the COVID-19 vaccines to aid public health officials in the battle against COVID-19.…”

Section: Social Media For Public Health Researchmentioning

confidence: 99%

Using Social Media to Predict Food Deserts in the United States: Infodemiology Study of Tweets

Sigalo¹,

Jean²,

Frías-Martínez³

2022

JMIR Public Health Surveill

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BackgroundThe issue of food insecurity is becoming increasingly important to public health practitioners because of the adverse health outcomes and underlying racial disparities associated with insufficient access to healthy foods. Prior research has used data sources such as surveys, geographic information systems, and food store assessments to identify regions classified as food deserts but perhaps the individuals in these regions unknowingly provide their own accounts of food consumption and food insecurity through social media. Social media data have proved useful in answering questions related to public health; therefore, these data are a rich source for identifying food deserts in the United States.ObjectiveThe aim of this study was to develop, from geotagged Twitter data, a predictive model for the identification of food deserts in the United States using the linguistic constructs found in food-related tweets.MethodsTwitter’s streaming application programming interface was used to collect a random 1% sample of public geolocated tweets across 25 major cities from March 2020 to December 2020. A total of 60,174 geolocated food-related tweets were collected across the 25 cities. Each geolocated tweet was mapped to its respective census tract using point-to-polygon mapping, which allowed us to develop census tract–level features derived from the linguistic constructs found in food-related tweets, such as tweet sentiment and average nutritional value of foods mentioned in the tweets. These features were then used to examine the associations between food desert status and the food ingestion language and sentiment of tweets in a census tract and to determine whether food-related tweets can be used to infer census tract–level food desert status.ResultsWe found associations between a census tract being classified as a food desert and an increase in the number of tweets in a census tract that mentioned unhealthy foods (P=.03), including foods high in cholesterol (P=.02) or low in key nutrients such as potassium (P=.01). We also found an association between a census tract being classified as a food desert and an increase in the proportion of tweets that mentioned healthy foods (P=.03) and fast-food restaurants (P=.01) with positive sentiment. In addition, we found that including food ingestion language derived from tweets in classification models that predict food desert status improves model performance compared with baseline models that only include socioeconomic characteristics.ConclusionsSocial media data have been increasingly used to answer questions related to health and well-being. Using Twitter data, we found that food-related tweets can be used to develop models for predicting census tract food desert status with high accuracy and improve over baseline models. Food ingestion language found in tweets, such as census tract–level measures of food sentiment and healthiness, are associated with census tract–level food desert status.

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Section: Social Media For Public Health Researchmentioning

confidence: 99%

Using Social Media to Predict Food Deserts in the United States: Infodemiology Study of Tweets

Sigalo¹,

Jean²,

Frías-Martínez³

2022

JMIR Public Health Surveill

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“…The most commonly derived main topic was the concern about AEs, which was the main topic reported in 6 studies [ 4 , 6 , 10 - 13 ]. Previous topic modeling studies have shown that vaccine efficacy [ 8 , 11 , 17 , 18 ] and hesitancy [ 4 , 5 , 19 ] are the most common topics. Vaccine development and progress were the second-most discussed topics, discussed in 5 studies [ 8 , 11 , 18 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

“…Previous topic modeling studies have shown that vaccine efficacy [ 8 , 11 , 17 , 18 ] and hesitancy [ 4 , 5 , 19 ] are the most common topics. Vaccine development and progress were the second-most discussed topics, discussed in 5 studies [ 8 , 11 , 18 , 20 , 21 ]. Certain people, such as Bill Gates and Kamala Harris, were mentioned as topic words [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…National vaccination policies, such as mask-wearing practices and social distancing measures, were important public discourse [ 12 , 22 , 23 ]. In addition, trust in the government and medical institutions [ 12 , 17 ], misinformation about vaccines [ 12 ], and conspiracy theories [ 18 ] have been discussed as major topics. Several topic modeling studies have identified supply [ 4 , 24 ], distribution [ 6 , 11 ], and access to vaccines [ 6 , 7 ] as important topics.…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Analysis of COVID-19 Vaccine Discourse by Vaccine Brand on Twitter in Korea: Topic and Sentiment Analysis

Park¹,

Suh²

2023

J Med Internet Res

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Background The unprecedented speed of COVID-19 vaccine development and approval has raised public concern about its safety. However, studies on public discourses and opinions on social media focusing on adverse events (AEs) related to COVID-19 vaccine are rare. Objective This study aimed to analyze Korean tweets about COVID-19 vaccines (Pfizer, Moderna, AstraZeneca, Janssen, and Novavax) after the vaccine rollout, explore the topics and sentiments of tweets regarding COVID-19 vaccines, and examine their changes over time. We also analyzed topics and sentiments focused on AEs related to vaccination using only tweets with terms about AEs. Methods We devised a sophisticated methodology consisting of 5 steps: keyword search on Twitter, data collection, data preprocessing, data analysis, and result visualization. We used the Twitter Representational State Transfer application programming interface for data collection. A total of 1,659,158 tweets were collected from February 1, 2021, to March 31, 2022. Finally, 165,984 data points were analyzed after excluding retweets, news, official announcements, advertisements, duplicates, and tweets with <2 words. We applied a variety of preprocessing techniques that are suitable for the Korean language. We ran a suite of analyses using various Python packages, such as latent Dirichlet allocation, hierarchical latent Dirichlet allocation, and sentiment analysis. Results The topics related to COVID-19 vaccines have a very large spectrum, including vaccine-related AEs, emotional reactions to vaccination, vaccine development and supply, and government vaccination policies. Among them, the top major topic was AEs related to COVID-19 vaccination. The AEs ranged from the adverse reactions listed in the safety profile (eg, myalgia, fever, fatigue, injection site pain, myocarditis or pericarditis, and thrombosis) to unlisted reactions (eg, irregular menstruation, changes in appetite and sleep, leukemia, and deaths). Our results showed a notable difference in the topics for each vaccine brand. The topics pertaining to the Pfizer vaccine mainly mentioned AEs. Negative public opinion has prevailed since the early stages of vaccination. In the sentiment analysis based on vaccine brand, the topics related to the Pfizer vaccine expressed the strongest negative sentiment. Conclusions Considering the discrepancy between academic evidence and public opinions related to COVID-19 vaccination, the government should provide accurate information and education. Furthermore, our study suggests the need for management to correct the misinformation related to vaccine-related AEs, especially those affecting negative sentiments. This study provides valuable insights into the public discourses and opinions regarding COVID-19 vaccination.

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“…In this retrospective observational event-driven infoveillance study, we sought to characterize public user reaction to the death of Hank Aaron on Twitter for purposes of expanding the literature on minority-specific COVID-19 misinformation topics as detected on social media platforms. Twitter was chosen for this study because previous research has shown that misinformation, and specifically vaccine-related misinformation and disinformation (including general antivaccination topics, misinformation about non-COVID-19 vaccines, and misinformation specific to COVID-19 vaccines), is prominent on the platform [3][4][5][6][7][8]. Additionally, information and news reports about both Aaron's initial vaccination and his death were shared on Twitter making it an ideal platform to further explore user interaction, sentiment, and dissemination of this content.…”

Section: Introductionmentioning

confidence: 99%

Spread of COVID-19 Vaccine Misinformation in the Ninth Inning: Retrospective Observational Infodemic Study

Calac¹,

Haupt²,

Bardier³

et al. 2022

JMIR Infodemiology

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Background Shortly after Pfizer and Moderna received emergency use authorizations from the Food and Drug Administration, there were increased reports of COVID-19 vaccine-related deaths in the Vaccine Adverse Event Reporting System (VAERS). In January 2021, Major League Baseball legend and Hall of Famer, Hank Aaron, passed away at the age of 86 years from natural causes, just 2 weeks after he received the COVID-19 vaccine. Antivaccination groups attempted to link his death to the Moderna vaccine, similar to other attempts misrepresenting data from the VAERS to spread COVID-19 misinformation. Objective This study assessed the spread of misinformation linked to erroneous claims about Hank Aaron’s death on Twitter and then characterized different vaccine misinformation and hesitancy themes generated from users who interacted with this misinformation discourse. Methods An initial sample of tweets from January 31, 2021, to February 6, 2021, was queried from the Twitter Search Application Programming Interface using the keywords “Hank Aaron” and “vaccine.” The sample was manually annotated for misinformation, reporting or news media, and public reaction. Nonmedia user accounts were also classified if they were verified by Twitter. A second sample of tweets, representing direct comments or retweets to misinformation-labeled content, was also collected. User sentiment toward misinformation, positive (agree) or negative (disagree), was recorded. The Strategic Advisory Group of Experts Vaccine Hesitancy Matrix from the World Health Organization was used to code the second sample of tweets for factors influencing vaccine confidence. Results A total of 436 tweets were initially sampled from the Twitter Search Application Programming Interface. Misinformation was the most prominent content type (n=244, 56%) detected, followed by public reaction (n=122, 28%) and media reporting (n=69, 16%). No misinformation-related content reviewed was labeled as misleading by Twitter at the time of the study. An additional 1243 comments on misinformation-labeled tweets from 973 unique users were also collected, with 779 comments deemed relevant to study aims. Most of these comments expressed positive sentiment (n=612, 78.6%) to misinformation and did not refute it. Based on the World Health Organization Strategic Advisory Group of Experts framework, the most common vaccine hesitancy theme was individual or group influences (n=508, 65%), followed by vaccine or vaccination-specific influences (n=110, 14%) and contextual influences (n=93, 12%). Common misinformation themes observed included linking the death of Hank Aaron to “suspicious” elderly deaths following vaccination, claims about vaccines being used for depopulation, death panels, federal officials targeting Black Americans, and misinterpretation of VAERS reports. Four users engaging with or posting misinformation were verified on Twitter at the time of data collection. Conclusions Our study found that the death of a high-profile ethnic minority celebrity led to the spread of misinformation on Twitter. This misinformation directly challenged the safety and effectiveness of COVID-19 vaccines at a time when ensuring vaccine coverage among minority populations was paramount. Misinformation targeted at minority groups and echoed by other verified Twitter users has the potential to generate unwarranted vaccine hesitancy at the expense of people such as Hank Aaron who sought to promote public health and community immunity.

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

Cited by 17 publications

References 69 publications

Using Social Media to Predict Food Deserts in the United States: Infodemiology Study of Tweets

Using Social Media to Predict Food Deserts in the United States: Infodemiology Study of Tweets

A Comprehensive Analysis of COVID-19 Vaccine Discourse by Vaccine Brand on Twitter in Korea: Topic and Sentiment Analysis

Spread of COVID-19 Vaccine Misinformation in the Ninth Inning: Retrospective Observational Infodemic Study

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