COVID-19 vaccination and the power of rumors: Why we must “Tune in”

Dasgupta, Rajib; Mishra, Pallavi; Yadav, Kavita

doi:10.4103/ijph.ijph_89_21

Cited by 6 publications

(4 citation statements)

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“…In India, the vaccine acceptance rate was ranged from 35% to 69% and the vaccine hesitancy ranged from 3.4% to 10%. [7][8][9][10] These studies reported lower vaccine hesitancy compared with other countries. A study of 944 Indians found 69% acceptance rate and 3.4% vaccine hesitancy among their participants.…”

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confidence: 74%

“…[11] Other studies showed that fear of ill health, lack of trust, less information on vaccine, and allergic reactions were most commonly cited reasons for negative attitudes. [4,7,8] Similarly, demographic, social, and contextual constructs were also associated with intention to vaccinate among the adult population. [3,5] Thus, in the background of highly contaminating and mutating COVID-19 virus and the unavailability of standardized tool to measures the attitude and concerns of Indian adults toward COVID-19 vaccination, the present study was undertaken to develop a short, standardized, and valid tool to assess the individual's attitudes and concerns toward COVID-19 vaccination among Indian adults.…”

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confidence: 99%

“…A study of 944 Indians found 69% acceptance rate and 3.4% vaccine hesitancy among their participants. [7,8] Another study reported that 70% participants had concerns regarding COVID-19 vaccine, 10% refuse to take vaccine, and 27% were not sure if they would get the vaccine. [9] Praveen, Ittamalla, and Deepak found positive attitudes toward vaccination in 35% participants.…”

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confidence: 99%

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Development and Validation of Attitudes and Concerns toward COVID-19 Vaccination Scale

Shaikh¹,

Pratika²

2022

APJHS

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Background: COVID-19 vaccines are one of the fastest developed vaccines to date. People have different views and opinions about it. A positive attitude can strengthen the vaccination program, whereas a negative attitude will be an obstacle for a healthy and safe nation. This study aimed at developing and validating the Attitudes and Concerns toward the COVID-19 vaccination. Methods: Initial draft of 48 items was developed based on literature review and interview of experts. After content validation with four experts and semantic validation with 20 respondents, 32 items were retained and administered to 607 Indian adults aged 18–60 years. Results: Data were analyzed by IBM-SPSS Version-23 with AMOS. Exploratory factor analysis supported the two-factor structure for the attitude toward COVID-19 vaccination and three factors structure for the concern domain. Cronbach alpha for “Vaccine Acceptance” and “Vaccine Hesitance” was.825 and.721. The reliability of religious concerns (α = 0.785), social concerns (α = 0.714), and health concern (α = 0.699) subscales was acceptable. The confirmatory factor analysis results verified two-factor model of attitude and three-factor model of concerns as the model indices were close to 1; RMSEA was 0.000 and PCLOSE values were 0.861 and 0.927, respectively, for Parts I and II. Conclusion: This is 12-item scale that measures vaccine acceptance, vaccine hesitance, religious concerns, social concerns, and health concerns related to COVID-19 vaccination.

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Development and Validation of Attitudes and Concerns toward COVID-19 Vaccination Scale

Shaikh¹,

Pratika²

2022

APJHS

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“…Research on health rumors on social media in the United States and India are mainly categorized as epidemic-related health information, information epidemic, and anti-vaccine and vaccine hesitancy. However, research on the health information that is widely circulated on the social media is lacking [ 19 , 20 ]. Some studies have mainly focused on Twitter, YouTube, Facebook, and other social media platforms [ 21 ].…”

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Hot Topic Recognition of Health Rumors Based on Anti-Rumor Articles on the WeChat Official Account Platform: Topic Modeling

Li¹,

Wu²,

Lin³

et al. 2023

J Med Internet Res

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Background Social networks have become one of the main channels for obtaining health information. However, they have also become a source of health-related misinformation, which seriously threatens the public’s physical and mental health. Governance of health-related misinformation can be implemented through topic identification of rumors on social networks. However, little attention has been paid to studying the types and routes of dissemination of health rumors on the internet, especially rumors regarding health-related information in Chinese social media. Objective This study aims to explore the types of health-related misinformation favored by WeChat public platform users and their prevalence trends and to analyze the modeling results of the text by using the Latent Dirichlet Allocation model. Methods We used a web crawler tool to capture health rumor–dispelling articles on WeChat rumor-dispelling public accounts. We collected information from health-debunking articles posted between January 1, 2016, and August 31, 2022. Following word segmentation of the collected text, a document topic generation model called Latent Dirichlet Allocation was used to identify and generalize the most common topics. The proportion distribution of the themes was calculated, and the negative impact of various health rumors in different periods was analyzed. Additionally, the prevalence of health rumors was analyzed by the number of health rumors generated at each time point. Results We collected 9366 rumor-refuting articles from January 1, 2016, to August 31, 2022, from WeChat official accounts. Through topic modeling, we divided the health rumors into 8 topics, that is, rumors on prevention and treatment of infectious diseases (1284/9366, 13.71%), disease therapy and its effects (1037/9366, 11.07%), food safety (1243/9366, 13.27%), cancer and its causes (946/9366, 10.10%), regimen and disease (1540/9366, 16.44%), transmission (914/9366, 9.76%), healthy diet (1068/9366, 11.40%), and nutrition and health (1334/9366, 14.24%). Furthermore, we summarized the 8 topics under 4 themes, that is, public health, disease, diet and health, and spread of rumors. Conclusions Our study shows that topic modeling can provide analysis and insights into health rumor governance. The rumor development trends showed that most rumors were on public health, disease, and diet and health problems. Governments still need to implement relevant and comprehensive rumor management strategies based on the rumors prevalent in their countries and formulate appropriate policies. Apart from regulating the content disseminated on social media platforms, the national quality of health education should also be improved. Governance of social networks should be clearly implemented, as these rapidly developed platforms come with privacy issues. Both disseminators and receivers of information should ensure a realistic attitude and disseminate health information correctly. In addition, we recommend that sentiment analysis–related studies be conducted to verify the impact of health rumor–related topics.

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Hot Topic Recognition of Health Rumors Based on Anti-Rumor Articles on the WeChat Official Account Platform: Topic Modeling (Preprint)

Li¹,

Wu²,

Lin³

et al. 2022

Preprint

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BACKGROUND Social network has become one of the main channels for the public to obtain health information. However, it has also become a source for the spread of health-related misinformation. Health-related misinformation seriously threatens the public’s physical and mental health. Topic identification is the premise of health-related misinformation governance. OBJECTIVE This study aims to explore the types of health-related misinformation favored by WeChat public platform users and their prevalence trends. METHODS We used a web crawler tool to capture health rumor-dispelling tweets collected on the rumor-dispelling public account. We collected text information from health-debunking tweets posted between January 1, 2016, and August 31, 2022. Following word segmentation of the collected text, a document topic generation model, Latent Dirichlet Assignment, is used to identify and generalize the most common topics. The proportion distribution of themes was calculated, and the negative impact of various health rumors in different periods was subsequently analyzed. Additionally, the prevalence of health rumors was analyzed using the number of health rumors generated at each time point. RESULTS From January 1, 2016 to August 31, 2022, we collected 9,366 rumor-refuting tweets from WeChat official accounts. Through topic modeling, we divided the health rumors into eight topics, including the prevention and treatment of infectious diseases (n = 1,284, 13.71%), disease therapy and its effects (n = 1,037, 11.07%), food safety (n = 1,243, 13.27%), cancer and its causes (n = 946, 10.10%), regimen and disease (n = 1,540, 16.44%), rumors of transmission (n = 914, 9.76%), healthy diet (n = 1,068, 11.40%), and nutrition and health (n = 1,334, 14.24%). Furthermore, we summarized the eight topics into four themes, including public health, disease, diet and health, and rumor spreading. CONCLUSIONS Our study shows that the topic model can provide analysis and insights into health rumor governance. The analysis of rumor development trends shows that public health, disease, and diet and health problems are the most affected areas of rumors. Governments still need to consider national conditions, formulate appropriate policies, and deal with health rumors more comprehensively. While ensuring the health of the Internet, we should also improve the level of national quality education. We recommend that additional sentiment analysis-related studies be conducted to verify the impact of health rumor-related topics.

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COVID-19 vaccination and the power of rumors: Why we must “Tune in”

Cited by 6 publications

References 1 publication

Development and Validation of Attitudes and Concerns toward COVID-19 Vaccination Scale

Development and Validation of Attitudes and Concerns toward COVID-19 Vaccination Scale

Hot Topic Recognition of Health Rumors Based on Anti-Rumor Articles on the WeChat Official Account Platform: Topic Modeling

Hot Topic Recognition of Health Rumors Based on Anti-Rumor Articles on the WeChat Official Account Platform: Topic Modeling (Preprint)

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