A new infodemiological approach through Google Trends: longitudinal analysis of COVID-19 scientific and infodemic names in Italy.
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ABSTRACT:
SUBMITTER: Rovetta A
PROVIDER: S-EPMC8801192 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundGoogle Trends is an infoveillance tool widely used by the scientific community to investigate different user behaviors related to COVID-19. However, several limitations regarding its adoption are reported in the literature.ObjectiveThis paper aims to provide an effective and efficient approach to investigating vaccine adherence against COVID-19 via Google Trends.MethodsThrough the cross-correlational analysis of well-targeted hypotheses, we investigate the predictive capacity of web searches related to COVID-19 toward vaccinations in Italy from November 2020 to November 2021. The keyword "vaccine reservation" query (VRQ) was chosen as it reflects a real intention of being vaccinated (V). Furthermore, the impact of the second most read Italian newspaper (vaccine-related headlines [VRH]) on vaccine-related web searches was investigated to evaluate the role of the mass media as a confounding factor. Fisher r-to-z transformation (z) and percentage difference (δ) were used to compare Spearman coefficients. A regression model V=f(VRH, VRQ) was built to validate the results found. The Holm-Bonferroni correction was adopted (P*). SEs are reported.ResultsSimple and generic keywords are more likely to identify the actual web interest in COVID-19 vaccines than specific and elaborated keywords. Cross-correlations between VRQ and V were very strong and significant (min r²=0.460, P*<.001, lag 0 weeks; max r²=0.903, P*<.001, lag 6 weeks). The remaining cross-correlations have been markedly lower (δ>55.8%; z>5.8; P*<.001). The regression model confirmed the greater significance of VRQ versus VRH (P*<.001 vs P=.03, P*=.29).ConclusionsThis research provides preliminary evidence in favor of using Google Trends as a surveillance and prediction tool for vaccine adherence against COVID-19 in Italy. Further research is needed to establish the appropriate use and limits of Google Trends for vaccination tracking. However, these findings prove that the search for suitable keywords is a fundamental step to reduce confounding factors. Additionally, targeting hypotheses helps diminish the likelihood of spurious correlations. It is recommended that Google Trends be leveraged as a complementary infoveillance tool by government agencies to monitor and predict vaccine adherence in this and future crises by following the methods proposed in this paper.
| S-EPMC9031689 | biostudies-literature
Project description:BackgroundSince the beginning of the novel coronavirus disease (COVID-19) outbreak, fake news and misleading information have circulated worldwide, which can profoundly affect public health communication.ObjectiveWe investigated online search behavior related to the COVID-19 outbreak and the attitudes of "infodemic monikers" (ie, erroneous information that gives rise to interpretative mistakes, fake news, episodes of racism, etc) circulating in Italy.MethodsBy using Google Trends to explore the internet search activity related to COVID-19 from January to March 2020, article titles from the most read newspapers and government websites were mined to investigate the attitudes of infodemic monikers circulating across various regions and cities in Italy. Search volume values and average peak comparison (APC) values were used to analyze the results.ResultsKeywords such as "novel coronavirus," "China coronavirus," "COVID-19," "2019-nCOV," and "SARS-COV-2" were the top infodemic and scientific COVID-19 terms trending in Italy. The top five searches related to health were "face masks," "amuchina" (disinfectant), "symptoms of the novel coronavirus," "health bulletin," and "vaccines for coronavirus." The regions of Umbria and Basilicata recorded a high number of infodemic monikers (APC weighted total >140). Misinformation was widely circulated in the Campania region, and racism-related information was widespread in Umbria and Basilicata. These monikers were frequently searched (APC weighted total >100) in more than 10 major cities in Italy, including Rome.ConclusionsWe identified a growing regional and population-level interest in COVID-19 in Italy. The majority of searches were related to amuchina, face masks, health bulletins, and COVID-19 symptoms. Since a large number of infodemic monikers were observed across Italy, we recommend that health agencies use Google Trends to predict human behavior as well as to manage misinformation circulation in Italy.
| S-EPMC7202310 | biostudies-literature
Project description:BackgroundCOVID-19 has caused the worst international crisis since World War II. Italy was one of the countries most affected by both the pandemic and the related infodemic. The success of anti-COVID-19 strategies and future public health policies in Italy cannot separate itself from the containment of fake news and the divulgation of correct information.ObjectiveThe aim of this paper was to analyze the impact of COVID-19 on web interest in conspiracy hypotheses and risk perception of Italian web users.MethodsGoogle Trends was used to monitor users' web interest in specific topics, such as conspiracy hypotheses, vaccine side effects, and pollution and climate change. The keywords adopted to represent these topics were mined from Bufale.net-an Italian website specializing in detecting online hoaxes-and Google Trends suggestions (ie, related topics and related queries). Relative search volumes (RSVs) of the time-lapse periods of 2016-2020 (pre-COVID-19) and 2020-2021 (post-COVID-19) were compared through percentage difference (∆%) and the Welch t test (t). When data series were not stationary, other ad hoc criteria were used. The trend slopes were assessed through Sen slope (SS). The significance thresholds have been indicatively set at P=.05 and t=1.9.ResultsThe COVID-19 pandemic drastically increased Italian netizens' interest in conspiracies (∆% ∈ [60, 288], t ∈ [6, 12]). Web interest in conspiracy-related queries across Italian regions increased and became more homogeneous compared to the pre-COVID-19 period (average RSV=80±2.8, t min=1.8, ∆min%=+12.4, min∆SD%=-25.8). In addition, a growing trend in web interest in the infodemic YouTube channel ByoBlu has been highlighted. Web interest in hoaxes has increased more than interest in antihoax services (t 1=11.3 vs t 2=4.5; Δ1%=+157.6 vs Δ2%=+84.7). Equivalently, web interest in vaccine side effects exceeded interest in pollution and climate change (SSvaccines=0.22, P<.001 vs SSpollution=0.05, P<.001; ∆%=+296.4). To date, a significant amount of fake news related to COVID-19 vaccines, unproven remedies, and origin has continued to circulate. In particular, the creation of SARS-CoV-2 in a Chinese laboratory constituted about 0.04% of the entire web interest in the pandemic.ConclusionsCOVID-19 has given a significant boost to web interest in conspiracy hypotheses and has made it more uniform across regions in Italy. The pandemic accelerated an already-growing trend in users' interest toward some fake news sources, including the 500,000-subscriber YouTube channel ByoBlu, which was removed from the platform by YouTube for disinformation in March 2021. The risk perception related to COVID-19 vaccines has been so distorted that vaccine side effect-related queries outweighed those relating to pollution and climate change, which are much more urgent issues. Moreover, a large amount of fake news has circulated about COVID-19 vaccines, remedies, and origin. Based on these findings, it is recommended that the Italian authorities implement more effective infoveillance systems, and that communication by the mass media be less sensationalistic and more consistent with the available scientific evidence. In this context, Google Trends can be used to monitor users' response to specific infodemiological countermeasures. Further research is needed to understand the psychological mechanisms that regulate risk perception.
| S-EPMC8363126 | biostudies-literature
Project description:BackgroundSince 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.MethodsData 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.ResultsIn 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.ConclusionsIn 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.
| S-EPMC9357377 | biostudies-literature
Project description:BackgroundPlague is a highly infectious zoonotic disease caused by the bacillus Yersinia pestis. Three major forms of the disease are known: bubonic, septicemic, and pneumonic plague. Though highly related to the past, plague still represents a global public health concern. Cases of plague continue to be reported worldwide. In recent months, pneumonic plague cases have been reported in Madagascar. However, despite such a long-standing and rich history, it is rather difficult to get a comprehensive overview of the general situation. Within the framework of electronic health (eHealth), in which people increasingly search the internet looking for health-related material, new information and communication technologies could enable researchers to get a wealth of data, which could complement traditional surveillance of infectious diseases.ObjectiveIn this study, we aimed to assess public reaction regarding the recent plague outbreak in Madagascar by quantitatively characterizing the public's interest.MethodsWe captured public interest using Google Trends (GT) and correlated it to epidemiological real-world data in terms of incidence rate and spread pattern.ResultsStatistically significant positive correlations were found between GT search data and confirmed (R2=0.549), suspected (R2=0.265), and probable (R2=0.518) cases. From a geospatial standpoint, plague-related GT queries were concentrated in Toamasina (100%), Toliara (68%), and Antananarivo (65%). Concerning the forecasting models, the 1-day lag model was selected as the best regression model.ConclusionsAn earlier digital Web search reaction could potentially contribute to better management of outbreaks, for example, by designing ad hoc interventions that could contain the infection both locally and at the international level, reducing its spread.
| S-EPMC6429048 | biostudies-literature
Project description:BackgroundGoogle Trends (GT) is being used as an epidemiological tool to study coronavirus disease (COVID-19) by identifying keywords in search trends that are predictive for the COVID-19 epidemiological burden. However, many of the earlier GT-based studies include potential statistical fallacies by measuring the correlation between non-stationary time sequences without adjusting for multiple comparisons or the confounding of media coverage, leading to concerns about the increased risk of obtaining false-positive results. In this study, we aimed to apply statistically more favorable methods to validate the earlier GT-based COVID-19 study results.MethodsWe extracted the relative GT search volume for keywords associated with COVID-19 symptoms, and evaluated their Granger-causality to weekly COVID-19 positivity in eight English-speaking countries and Japan. In addition, the impact of media coverage on keywords with significant Granger-causality was further evaluated using Japanese regional data.ResultsOur Granger causality-based approach largely decreased (by up to approximately one-third) the number of keywords identified as having a significant temporal relationship with the COVID-19 trend when compared to those identified by Pearson or Spearman's rank correlation-based approach. "Sense of smell" and "loss of smell" were the most reliable GT keywords across all the evaluated countries; however, when adjusted with their media coverage, these keyword trends did not Granger-cause the COVID-19 positivity trends (in Japan).ConclusionsOur results suggest that some of the search keywords reported as candidate predictive measures in earlier GT-based COVID-19 studies may potentially be unreliable; therefore, caution is necessary when interpreting published GT-based study results.
| S-EPMC8286439 | biostudies-literature
Project description:For more than 250 years, the taxonomic enterprise has remained almost unchanged. Certainly, the tools of the trade have improved: months-long journeys aboard sailing ships have been reduced to hours aboard jet airplanes; advanced technology allows humans to access environments that were once utterly inaccessible; GPS has replaced crude maps; digital hi-resolution imagery provides far more accurate renderings of organisms that even the best commissioned artists of a century ago; and primitive candle-lit microscopes have been replaced by an array of technologies ranging from scanning electron microscopy to DNA sequencing. But the basic paradigm remains the same. Perhaps the most revolutionary change of all - which we are still in the midst of, and which has not yet been fully realized - is the means by which taxonomists manage and communicate the information of their trade. The rapid evolution in recent decades of computer database management software, and of information dissemination via the Internet, have both dramatically improved the potential for streamlining the entire taxonomic process. Unfortunately, the potential still largely exceeds the reality. The vast majority of taxonomic information is either not yet digitized, or digitized in a form that does not allow direct and easy access. Moreover, the information that is easily accessed in digital form is not yet seamlessly interconnected. In an effort to bring reality closer to potential, a loose affiliation of major taxonomic resources, including GBIF, the Encyclopedia of Life, NBII, Catalog of Life, ITIS, IPNI, ICZN, Index Fungorum, and many others have been crafting a "Global Names Architecture" (GNA). The intention of the GNA is not to replace any of the existing taxonomic data initiatives, but rather to serve as a dynamic index to interconnect them in a way that streamlines the entire taxonomic enterprise: from gathering specimens in the field, to publication of new taxa and related data.
| S-EPMC4741226 | biostudies-other
Project description:BackgroundWeb-based analysis of search queries has become a very useful method in various academic fields for understanding timely and regional differences in the public interest in certain terms and concepts. Particularly in health and medical research, Google Trends has been increasingly used over the last decade.ObjectiveThis study aimed to assess the search activity of pain-related parameters on Google Trends from among the most populated regions worldwide over a 3-year period from before the report of the first confirmed COVID-19 cases in these regions (January 2018) until December 2020.MethodsSearch terms from the following regions were used for the analysis: India, China, Europe, the United States, Brazil, Pakistan, and Indonesia. In total, 24 expressions of pain location were assessed. Search terms were extracted using the local language of the respective country. Python scripts were used for data mining. All statistical calculations were performed through exploratory data analysis and nonparametric Mann-Whitney U tests.ResultsAlthough the overall search activity for pain-related terms increased, apart from pain entities such as headache, chest pain, and sore throat, we observed discordant search activity. Among the most populous regions, pain-related search parameters for shoulder, abdominal, and chest pain, headache, and toothache differed significantly before and after the first officially confirmed COVID-19 cases (for all, P<.001). In addition, we observed a heterogenous, marked increase or reduction in pain-related search parameters among the most populated regions.ConclusionsAs internet searches are a surrogate for public interest, we assume that our data are indicative of an increased incidence of pain after the onset of the COVID-19 pandemic. However, as these increased incidences vary across geographical and anatomical locations, our findings could potentially facilitate the development of specific strategies to support the most affected groups.
| S-EPMC8064706 | biostudies-literature
Project description:Google Flu Trends (GFT) uses Internet search queries in an effort to provide early warning of increases in influenza-like illness (ILI). In the United States, GFT estimates the percentage of physician visits related to ILI (%ILINet) reported by the Centers for Disease Control and Prevention (CDC). However, during the 2012-13 influenza season, GFT overestimated %ILINet by an appreciable amount and estimated the peak in incidence three weeks late. Using data from 2010-14, we investigated the relationship between GFT estimates (%GFT) and %ILINet. Based on the relationship between the relative change in %GFT and the relative change in %ILINet, we transformed %GFT estimates to better correspond with %ILINet values. In 2010-13, our transformed %GFT estimates were within ± 10% of %ILINet values for 17 of the 29 weeks that %ILINet was above the seasonal baseline value determined by the CDC; in contrast, the original %GFT estimates were within ± 10% of %ILINet values for only two of these 29 weeks. Relative to the %ILINet peak in 2012-13, the peak in our transformed %GFT estimates was 2% lower and one week later, whereas the peak in the original %GFT estimates was 74% higher and three weeks later. The same transformation improved %GFT estimates using the recalibrated 2013 GFT model in early 2013-14. Our transformed %GFT estimates can be calculated approximately one week before %ILINet values are reported by the CDC and the transformation equation was stable over the time period investigated (2010-13). We anticipate our results will facilitate future use of GFT.
| S-EPMC4281210 | biostudies-literature