Moreover, the WHO itself has launched an international campaign called “Managing the COVID-19 infodemic: Promoting healthy behaviors and mitigating the harm from misinformation and disinformation” to sensitize states to contrast the spread of misinformation ( WHO, 2020). Dr 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 ( UNS, 2020). In this scenario, fake news and inaccurate information circulated widely on the web creating severe issues to public health and economy all over the world ( Pennycook et al., 2020 Rovetta and Bhagavathula, 2020 Tagliabue et al., 2020 Tasnim et al., 2020). Such pandemic has put a strain on health systems and economies of countries worldwide, causing more than 3 million deaths and forcing governments to implement very restrictive lockdowns ( Askitas et al., 2021). Despite attempts by governments and the scientific community to contain the infection, COVID-19 has spread beyond the Chinese borders and was declared a pandemic by the WHO on Ma( Cascella et al., 2021). The virus, formerly reported as 2019-nCoV, was subsequently named SARS-CoV-2. This was responsible for a severe respiratory disease named COVID-19 by the WHO on February 11, 2020. Further research is needed to evaluate the effectiveness of this method.Ī novel coronavirus was identified in Wuhan (Hubei province, China) in late 2019 ( Wu et al., 2020). Since RSVs values showed a high dependence on the day they were gathered, it is essential for future research that the authors collect queries’ data for several consecutive days and work with their RSVs averages instead of daily RSVs, trying to minimize the standard errors until an established confidence threshold is respected. Finally, the large amount of anomalies registered in Italian and international cities’ RSVs made these datasets unusable for any kind of statistical inference.Ĭonclusion: In the considered timespans, Google Trends has proved to be reliable only for surveys concerning RSVs of countries worldwide. Furthermore, only RSVs of countries worldwide did not exceed confidence threshold. However, the correlations between RSVs and COVID-19 cases underwent significant variations even in these two datasets ( M a x | Δ | = + 625 % for Italian regions, and M a x | Δ | = + 175 % for countries worldwide). Results: Google Trends has been subject to an acceptable quantity of anomalies only as regards the RSVs of Italian regions (0% in both periods 1 and 2) and countries worldwide (9.7% during period 1 and 10.9% during period 2). The percentage increase Δ was used to quantify the difference between two values. A dataset was deemed unreliable if the confident data exceeded 20% (confidence threshold). Two RSVs were considered statistical confident when t < 1.5. Welch’s 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 X i = X ( σ i, x ¯ i ) was used to represent the trend of daily variations of x i j = R S V s i j. 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. In particular, the paper focuses on the analysis of relative search volumes (RSVs) quantifying their dependence on the day they are collected. Objective: The aim of this study is to test the reliability of a widely used infoveillance tool which is Google Trends. In this context, the use of infoveillance tools has become a primary necessity.
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