Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jan 27, 2022
Date Accepted: Apr 4, 2022
Date Submitted to PubMed: Apr 5, 2022
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
The interaction of internet media news and vaccination decisions.
ABSTRACT
Background:
Vaccination is one way to prevent the incidence and spread of serious disease. Many factors influence the public's decision to vaccinate, including Internet information. Misinformation is a critical issue, and can be hard to detect, although it can change people's minds, opinions, and decisions. The impact of misinformation on public health and vaccination hesitancy is well documented, but little research has been conducted into the relationship between the size of the population reached by misinformation and vaccination decisions. A number of fact-checking services are available on the web, and the Islander news analysis system is a free web service that provides individuals with real-time web news judgment. In this study we used such services to estimate the amount of misinformation, and used Google Trends levels to model the spread of misinformation. We quantified this relationship using official public data on COVID-19 vaccination in Taiwan.
Objective:
In this study we aimed to quantify the impact of the magnitude of the propagation of misinformation on vaccination decisions.
Methods:
We collected public data about COVID-19 infections and vaccination from Taiwan's official website, and estimated the search popularity level using Google Trends. We indirectly collected news from 26 Internet media sources, using the news database of the Islander system. This system crawls the Internet in real-time, analyzes the news, and stores it. The incitement and suspicion scores of the Islander system were used to objectively judge news, and a suspicious news percentage variable based on its distribution was produced. We used multivariable linear regression, chi-squared tests, and the Johnson-Neyman procedure to analyze this relationship, using weekly data.
Results:
A total of 791,183 news items were obtained from 43 weeks in 2021, with a significant increase in the suspicious news percentage in 11 of the 26 media during the public vaccination stage. The regression model revealed a positive adjusted coefficient (0.98, p-value =.002) of available vaccine availability on the next week's vaccination doses, and a negative adjusted coefficient (-3.21, p-value =.035) of the interaction term on the suspicious news percentage with Google Trends level. The Johnson-Neiman plot of the adjusted effect for the interaction term shows that the Google Trends level had a significant negative adjustment effect on vaccination doses for the following week when the percent of suspicious news exceeded 39.3%.
Conclusions:
There was a significant relationship between the size of the population exposed to suspicious news and the number of vaccination doses. Reducing the amount of suspicious news and increasing public immunity to misinformation will be a critical issue in the Internet age. Clinical Trial: None
Citation
