The COVID-19 infodemic in Brazil: trends in Google search data

Abstract

Background: Since the beginning of the new coronavirus pandemic, there has been much information about the disease and the virus has been in the spotlight, shared and commented upon on the Internet. However, much of this information is infodemics and can interfere with the advancement of the disease and that way that populations act. Thus, Brazil is a country that requires attention, as despite the fact that in almost two years of pandemic it has shown a devastating numbers of deaths and number of cases, and generates false, distorted and malicious news about the pandemic. This work intends to understand the attitudes of the Brazilian population using infodemic queries from the Google Trends search tool and social and income variables.

Methods: Data from infodemic research carried out on Google Trends, between January 1, 2020 and June 30, 2021, with socioeconomic data, such as income and education, were unified in a single database: standardization and exploratory and multivalued techniques based on grouping were used in the study.

Results: In the analysis of the search trend of infodemic terms, it is clear that the categories of Prevention and Beliefs should stand out in Brazil, where there is a diverse culture. It is followed by the COVID-19 Treatment category, with treatments that were not those recommended by the authorities. Income transfer programs and information on socioeconomic variables did not have much impact on infodemic surveys, but it was observed that states where President Bolsonaro has more supporters had researched more infodemic information.

Conclusions: In a country as geographically large as Brazil, it is important that political authorities go to great lengths to disseminate reliable information and monitor the infodemic in the media and on the internet. It was concluded that the denial of the pandemic and the influence of political leaders influenced the search for infodemic information, contributing to a disorganization in the control of the disease and prevention measures.

Keywords: Brazil; COVID-19; Clustering; Denialism; Google Trends; Infodemic.

Figure 1. Clustering algorithm methodology.

The steps are presented in the order of execution: data treatment with the z-score technique, dissimilarity coefficient with the chosen Euclidean method and the cluster analysis step, where the cophenetic correlation coefficient (CCC) was calculated, generated the dendrograms and the Elbow method was used in parallel with the dendrograms.

Figure 2. Infodemic searches in the GT in the period of 18 months.

Eighteen months of research for all terms in the five categories studied, between January 1, 2020 and June 30, 2021, divided into six quarters.

Figure 3. Data from infodemics RSVs, income and education.

The columns represent the 18-month average of information for each Brazilian state: The first column is the average per capita income in Brazilian currency; the second column represents the average, in percentage, of the level of education in completed secondary education and the last column the average of infodemics RSVs.

Figure 4. Brazilian regions grouped by infodemic searches and social differences.

The multivalued analysis technique was applied in each quarter, grouping Brazil into a different format of the Brazilian regions: what can be observed in each column representing a quarter. It presents satisfactory CCC (0.89, 0.88, 0.91, 0.90, 0.87 and 0.91, respectively, for each pandemic quarter) and the number of clusters was 6, 6, 5, 7, 5 and 6.

Figure 5. Data from infodemic RSVs and income distribution programs.

The columns represent the average of 18 months of information for each Brazilian state: the first column is the average payment of emergency aid in local currency; the second column represents the average number of unemployment insurance claims and the last column the average of RSVs infodemics.

Figure 6. Brazilian regions grouped by infodemic searches and money transfer programs.

The multivalued analysis technique was applied in each quarter, grouping Brazil into a different format of the Brazilian regions: what can be observed in each column representing a quarter. It presents satisfactory CCC (0.82, 0.78, 0.78, 0.82, 0.80 and 0.77, respectively, for each pandemic quarter) and the number of clusters was six, seven, six, six, seven and six.

Figure 7. Data from infodemic RSVs and 2018 elections.

The first column represents the percentage of votes from each state for the elected president, and the last column represents the average of the RSVs infodemics.

Figure 8. Brazilian regions grouped by infodemic searches and 2018 elections.

The multivalued analysis technique was applied throughout the period, generating clusters presented in the column with satisfactory CCC (0.86), and having the number 6 clusters.