Furthermore, some anomalies in the calculus of relative search volumes (RSVs) could also alter any infodemiological analysis in an unpredictable way. However, not all that glitters is gold: indeed, Google Trends has some limitations that are often overlooked and which risk heavily biasing and distorting correlation-based analytics. This type of study is based on the search for statistical cross-correlations between users’ web searches related to specific diseases, such as symptoms, drugs, therapies, vaccines, number of infected people, number of deaths, etc., and the number of disease contagions and deaths officially registered after a certain timespan. In particular, Google Trends-an open online infoveillance tool developed by Google™ - has been widely used by the scientific community not only as for quantifying disinformation but also to make epidemiological predictions on the spread of infectious diseases, including COVID-19. In this regard, scientists are increasingly adopting infoveillance tools to monitoring the infodemic on websites, social media, and newspapers. Therefore, the demand for new effective and efficient infodemiological methods has never been as pressing as today. To date, one of the main problems consists in conspiracy news relating to alleged vaccine damage, which can seriously compromise the international strategy for the abatement of SARS-CoV-2. Moreover, the WHO itself has launched an international campaign called “Managing the COVID-19 infodemic: Promoting healthy behaviours and mitigating the harm from misinformation and disinformation” to sensitize states to contrast the spread of misinformation. Tedros Adhanom Ghebreyesus-director of the World Health Organization (WHO)- claimed that the battle we are fighting does not only concern the epidemic but also its infodemic. The percentage increase Δ was used to quantify the difference between two values.ĭuring the COVID-19 pandemic, fake news and inaccurate information circulated widely on the web creating severe issues to public health and economy all over the world. A dataset was deemed unreliable if the confident data exceeded 20% (confidence threshold). Two RSVs were considered statistical confident when t < 1.5. Student t-test was used to assess the statistical significance of the differences between the average RSVs of the various countries, regions, or cities of a given dataset. Pearson and Spearman correlations between RSVs and the number of COVID-19 cases were calculated day by day thus to highlight any variations related to the day RSVs were collected. When the anomalies exceeded 20% of the sample size, the whole sample was excluded from the statistical analysis. When a missing value was revealed (anomaly), the affected country, region or city was excluded from the analysis. To do this, by calling i the country, region, or city under investigation and j the day its RSV was collected, a Gaussian distribution was used to represent the trend of daily variations of x ij = RSVs ij. Each dataset was analyzed to observe any dependencies of RSVs from the day they were gathered. The search category was set to all categories. The survey covered Italian regions and cities, and countries and cities worldwide. Methods RSVs of the query coronavirus + covid during February 1 - Decem(period 1), and February 20 - (period 2), were collected daily by Google Trends from December 8 to 27, 2020.
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