JMIR Publications

ABSTRACT

Background:

As of December 2022, the outbreak of COVID-19 showed no sign of abating, continuing to impact people's lives, livelihoods, economies, and more. Vaccination is an effective way to achieve mass immunity, which is indispensable for controlling infectious diseases. However, in places such as Japan, where vaccination is voluntary, there are people who choose to not receive the vaccine, even if an effective vaccine is offered. It has been noted that information on social media influences vaccination intentions. Thus, to promote vaccination, it is necessary to clarify what kind of information can influence attitudes towards vaccines.

Objective:

False rumors are often posted and spread in large numbers on social media, especially during emergencies. Therefore, we analyze tweets with indications of false rumors related to the COVID-19 vaccine on Twitter. We defined a tweet indicating a false rumor (TIFR) as one that contains questions or points out errors of information. We aimed to answer the following questions: (1) what kinds of TIFRs about the COVID-19 vaccine were posted on Twitter, and (2) are the number of posted TIFRs related to social conditions such as vaccination status.

Methods:

We use the following data sets: (1) the number of TIFRs automatically collected by the "Rumor Cloud", and (2) the number of COVID-19 vaccine inoculators published on the website of the Prime Minister's Office. First, we counted the number of COVID-19 vaccine-related TIFRs from data set (1). Then, we examined the cross-correlation coefficients between data set (1) and data set (2). Through this verification, we examined the correlation coefficients for the following three periods: (A) the same period of data, (B) the case where the occurrence of the suggestion of TIFRs precedes the vaccination (negative time lag), and (C) the case where the vaccination precedes the occurrence of TIFRs (positive time lag). The data period used for the validation was from October 4, 2021 to April 18, 2022.

Results:

The correlation coefficients between the number of TIFRs and vaccine inoculators showed significant and strong positive correlations. The correlation coefficient was over 0.7 at -8, -7, and -1 weeks of lag. Results suggests that the number of vaccine inoculators tended to increase with an increase in the number of TIFRs. Significant correlation coefficients of 0.5 to 0.6 were observed for lag +1 week and +2 weeks. This implies that an increase of vaccine inoculators increases the number of TIFRs.

Conclusions:

This study revealed the following two findings: (1) most TIFRs about the COVID-19 vaccine were negative. (2) There was a positive correlation between the number of TIFRs and the number of COVID-19 vaccinations. Our results suggest that the increase in the number of TIFRs may have been a factor in inducing vaccination behavior.