Misinformation, believability, and vaccine acceptance over 40 countries: Takeaways from the initial phase of the COVID-19 infodemic

Abstract

The COVID-19 pandemic has been damaging to the lives of people all around the world. Accompanied by the pandemic is an infodemic, an abundant and uncontrolled spread of potentially harmful misinformation. The infodemic may severely change the pandemic's course by interfering with public health interventions such as wearing masks, social distancing, and vaccination. In particular, the impact of the infodemic on vaccination is critical because it holds the key to reverting to pre-pandemic normalcy. This paper presents findings from a global survey on the extent of worldwide exposure to the COVID-19 infodemic, assesses different populations' susceptibility to false claims, and analyzes its association with vaccine acceptance. Based on responses gathered from over 18,400 individuals from 40 countries, we find a strong association between perceived believability of COVID-19 misinformation and vaccination hesitancy. Our study shows that only half of the online users exposed to rumors might have seen corresponding fact-checked information. Moreover, depending on the country, between 6% and 37% of individuals considered these rumors believable. A key finding of this research is that poorer regions were more susceptible to encountering and believing COVID-19 misinformation; countries with lower gross domestic product (GDP) per capita showed a substantially higher prevalence of misinformation. We discuss implications of our findings to public campaigns that proactively spread accurate information to countries that are more susceptible to the infodemic. We also defend that fact-checking platforms should prioritize claims that not only have wide exposure but are also perceived to be believable. Our findings give insights into how to successfully handle risk communication during the initial phase of a future pandemic.

Fig 1. Distribution of study participants around the world.

The study obtained responses from 18,407 participants from 40 countries.

Fig 2. Sample and population age distributions for Brazil (N = 698).

As an example, this figures highlights the differences between weighted and unweighted exposure rates to the 5G claim. Error bars are standard errors.

Fig 3. Country-level exposure to rumors and fact-checks.

The pink polygon presents the weighted percentage of people who have been exposed to rumors. The purple polygon shows exposure to fact-checks.

Fig 4. Scatter plot and linear relationship between country-level exposure to rumors and ranked GDP per capita.

The x-axis represents the ranked GDP per capita values of countries in our study. Spearman correlation values and their respective significance levels are also presented. The rightmost bottom plot presents the results across all claims. Significance marked as * p<.05, ** p<.01, *** p<.001.

Fig 5. Relative perceived believability of each rumor addressed in this study.

Country-level z-scores are presented. The countries on the x-axis are in increasing order of GDP per capita.

Fig 6. Model 3’s marginal effects of all predictors and their 95% confidence intervals.

Variables are color-coded as per the groups. The horizontal dashed lines indicate Exposure, Believability and Fact Checks for different groups.

Fig 7. Comparison of claims ranked based on (a) heuristic algorithmic prioritization and (b) how currently practiced.

(a) Blind belief scale (% of respondents who will likely believe in claims upon exposure, without having access to fact-checks). (b) Aggregate dissemination percentage of fact-checks.