Search

The current study aimed to address the limitations of the terror management theory literature by using big data analysis to examine the theory's predictions in the COVID-19 pandemic. Specifically, Google Trends were examined before and after the first COVID-19 case was identified in Singapore. The results showed that there was a significant increase in mortality salience, intergroup conflict, and prosocial behavior, and a significant decrease in materialism after the first COVID-19 case was identified. However, no significant differences were found for anxiety. Limitations include the assumption that search terms reflect intentions that would eventually lead to a relevant behavior and the lack of data from other sources to corroborate with the results from Google Trends. Future research could use data from other sources to examine the effects of COVID-19 on theoretically relevant behaviors., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose: To analyse patterns of stone disease online information-seeking behaviours in the United States and to correlate with urological literature publication aspects., Methods: To compare Relative Search Volume (RSV) among different twelve preselected urologic keywords we chose "United States" as country and "01/01/2009-31/12/2018" as time range on Google Trends (GT). We defined "ureteroscopy" as a reference and compared RSV against it for each term. RSV was adjusted and normalized in a scale 0-100. Trend presence was evaluated by Mann-Kendall Test and magnitude by Sen's Slope Estimator (SS). Weather influence on RSV was also investigated by comparison of the ten hottest versus ten coldest states. Pearson correlation analysis was performed between number of Pubmed publications and RSV for each term over time., Results: We found an upward tendency (p < 0.01) for most terms. Higher temporal trends were seen for "kidney stone" (SS = 0.36), "kidney pain" (SS = 0.39) and "tamsulosin" (SS = 0.21). Technical treatment terms had little search volumes and no increasing trend. States with hotter weather showed higher mean RSV for "kidney stone" than colder ones. There was little correlation between GT and Pubmed for most terms, with the exception of "kidney stone" (R = 0.89; p < 0.01), "URS" (R = 0.81; p < 0.01), and "laser lithotripsy" (R = 0.74; p = 0.01)., Conclusion: There was a significant increase in online search for medical information related to stone disease. Citizens tend to look for generic terms related to symptoms or the disease itself. States with hotter weather show higher RSV than colder states. There is a discrepancy between public and medical community medical terms., (© 2020. Springer-Verlag GmbH Germany, part of Springer Nature.)

Background & Objective: Nutritional culturomics (NCs) is a specific focus area of culturomics epistemology developing digital humanities and computational linguistics approaches to search for macro-patterns of public interest in food, nutrition and diet choice as a major component of cultural evolution. Cultural evolution is considered as a driver at the interface of environmental and food science, economy and policy., Methods: The paper presents an epistemic programme that builds on the use of big data from webbased services such as Google Trends, Google Adwords or Google Books Ngram Viewer., Results: A comparison of clearly defined NCs in terms of geography, culture, linguistics, literacy, technological setups or time period might be used to reveal variations and singularities in public's behavior in terms of adaptation and mitigation policies in the agri-food and public health sectors., Conclusion: The proposed NC programme is developed along major axes: (1) the definition of an NC; (2) the reconstruction of food and diet histories; (3) the nutrition related epidemiology; (4) the understanding of variability of NCs; (5) the methodological diversification of NCs; (6) the quantifiable limitations and flaws of NCs. A series of indicative examples are presented regarding these NC epistemology components., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Franzén (2023 : 1) has warned of 'big problems' when researchers attempt to use Google Trends (GT) data. The evidence she provides is examined, additional evidence is obtained and analysed, and a new set of conclusions are derived. The anomalies previously encountered are due to a combination of factors, but can be explained by noting that Google samples its data to provide GT results, these data are also scaled, which can exacerbate variation between samples, and Denmark is a small country and Jakob Scharf a low-probability search term, both of which would increase variation in search probabilities provided by GT. When multiple samples are obtained and aggregated (medians are best for low-probability search terms), this variation is controlled, and a stable time series is derived. Researchers should not see GT as an easy source of data, but should do the work required to understand the data, and should use it, and interpret their results, within the limitations inherent in these data. It is important to aggregate multiple samples (preferably with the median for each time point) in order to obtain more stable estimates from GT. [ABSTRACT FROM AUTHOR]

Background: Influenza-like illness (ILI) encompasses symptoms similar to influenza, affecting population health. Surveillance, including Google Trends (GT), offers insights into epidemic patterns. Methods: This study used multiple regression models to analyze the correlation between ILI incidents, GT keyword searches, and climate variables during influenza outbreaks. It compared the predictive capabilities of time-series and deep learning models against ILI emergency incidents. Results: The GT searches for "fever" and "cough" were significantly associated with ILI cases (p < 0.05). Temperature had a more substantial impact on ILI incidence than humidity. Among the tested models, ARIMA provided the best predictive power. Conclusions: GT and climate data can forecast ILI trends, aiding governmental decision making. Temperature is a crucial predictor, and ARIMA models excel in forecasting ILI incidences. [ABSTRACT FROM AUTHOR]

Facing challenges like the COVID-19 pandemic, statistical production increasingly relies on non-traditional data sources for timely and accurate information. In this regard, The National Statistical Office of Colombia (DANE, by its acronym in Spanish) initiated a project, supported by the Statistics Advisory Council, to develop an early estimator for the Colombian agricultural sector. This paper presents the results for the implementation of a Ridge model and Zero Shot Classification to estimate the Gross Domestic Product (GDP) of the agricultural sector, leveraging Google News and Google Trends. Results reveal that these alternative sources offer valuable insights into economic trends. Combining machine learning techniques with Nowcasting methods yielded precise projections. The Ridge method demonstrated the lowest estimation error, providing an early GDP indicator for the agricultural sector of 8,188 billion Colombian pesos for 2022 Q2, 30 days ahead of official publication.

The ongoing depletion of natural systems and associated biodiversity decline is of growing international concern. Climate change is expected to exacerbate anthropogenic impacts on wild populations. The scale of impact on ecosystems and ecosystem services will be determined by the impact on a multitude of species and functional groups, which due to their biology and numbers are difficult to monitor. The IPCC has argued that surveillance or monitoring is critical and proposed that monitoring systems should be developed, which not only track developments but also function as "early warning systems." Human populations are already generating large continuous datasets on multiple taxonomic groups through internet searches. These time series could in principle add substantially to current monitoring if they reflect true changes in the natural world. We here examined whether information on internet search frequencies delivered by the Danish population and captured by Google Trends (GT) appropriately informs on population trends in 106 common Danish bird species. We compared the internet search activity with independent equivalent population trend assessments from the Danish Ornithological Society (BirdLife Denmark/DOF). We find a fair concordance between the GT trends and the assessments by DOF. A substantial agreement can be obtained by omitting species without clear temporal trends. Our findings suggest that population trend proxies from internet search frequencies can be used to supplement existing wildlife population monitoring and to ask questions about an array of ecological phenomena, which potentially can be integrated into an early warning system for biodiversity under climate change. [ABSTRACT FROM AUTHOR]

Google Trends (GT) data could potentially supplement traditional crime measurement strategies, allowing criminologists to better understand crime statistics on a macro level. This study assesses the validity of GT crime estimates. The findings indicate that GT data are reliable for estimating MVT, larceny, and rape. Additionally, we illustrate how to use GT to identify places with high rates of unreported offenses. The results of this study demonstrate the feasibility of leveraging open-source big data such as GT to supplement traditional sources of crime data, particularly for categories of crime with substantial underreporting rates. Results suggest the GT rape measure may be a more accurate estimate of the true incidence of rape than the measure drawn from the Uniform Crime Report (UCR). The limitations associated with the use of GT to generate estimates of crime are also discussed. [ABSTRACT FROM AUTHOR]

During virus outbreaks in the recent past, web behavior mining, modeling, and analysis have served as means to examine, explore, interpret, assess, and forecast the worldwide perception, readiness, reactions, and response linked to these virus outbreaks. The recent outbreak of the Marburg Virus disease (MVD), the high fatality rate of MVD, and the conspiracy theory linking the FEMA alert signal in the United States on 4 October 2023 with MVD and a zombie outbreak, resulted in a diverse range of reactions in the general public which has transpired in a surge in web behavior in this context. This resulted in "Marburg Virus" featuring in the list of the top trending topics on Twitter on 3 October 2023, and "Emergency Alert System" and "Zombie" featuring in the list of top trending topics on Twitter on 4 October 2023. No prior work in this field has mined and analyzed the emerging trends in web behavior in this context. The work presented in this paper aims to address this research gap and makes multiple scientific contributions to this field. First, it presents the results of performing time-series forecasting of the search interests related to MVD emerging from 216 different regions on a global scale using ARIMA, LSTM, and Autocorrelation. The results of this analysis present the optimal model for forecasting web behavior related to MVD in each of these regions. Second, the correlation between search interests related to MVD and search interests related to zombies was investigated. The findings show that there were several regions where there was a statistically significant correlation between MVD-related searches and zombie-related searches on Google on 4 October 2023. Finally, the correlation between zombie-related searches in the United States and other regions was investigated. This analysis helped to identify those regions where this correlation was statistically significant. [ABSTRACT FROM AUTHOR]

The World Health Organization (WHO) added Disease X to their shortlist of blueprint priority diseases to represent a hypothetical, unknown pathogen that could cause a future epidemic. During different virus outbreaks of the past, such as COVID-19, Influenza, Lyme Disease, and Zika virus, researchers from various disciplines utilized Google Trends to mine multimodal components of web behavior to study, investigate, and analyze the global awareness, preparedness, and response associated with these respective virus outbreaks. As the world prepares for Disease X, a dataset on web behavior related to Disease X would be crucial to contribute towards the timely advancement of research in this field. Furthermore, none of the prior works in this field have focused on the development of a dataset to compile relevant web behavior data, which would help to prepare for Disease X. To address these research challenges, this work presents a dataset of web behavior related to Disease X, which emerged from different geographic regions of the world, between February 2018 and August 2023. Specifically, this dataset presents the search interests related to Disease X from 94 geographic regions. These regions were chosen for data mining as these regions recorded significant search interests related to Disease X during this timeframe. The dataset was developed by collecting data using Google Trends. The relevant search interests for all these regions for each month in this time range are available in this dataset. This paper also discusses the compliance of this dataset with the FAIR principles of scientific data management. Finally, an analysis of this dataset is presented to uphold the applicability, relevance, and usefulness of this dataset for the investigation of different research questions in the interrelated fields of Big Data, Data Mining, Healthcare, Epidemiology, and Data Analysis with a specific focus on Disease X. [ABSTRACT FROM AUTHOR]

Google Trends data can be informative for zoonotic disease incidences, including Lyme disease. However, the use of Google Trends for predictive purposes is underutilized. In this study, we demonstrate the potential to use Google Trends for zoonotic disease prediction by predicting monthly state-level Lyme disease case counts in the United States. We requested Lyme disease data for the years 2010–2021. We downloaded Google Trends search data on terms for Lyme disease, symptoms of Lyme disease, and diseases with similar symptoms to Lyme disease. For each search term, we built an expanding window negative binomial model that adjusted for seasonal differences using a lag term. Performance was measured by Root Mean Squared Errors (RMSEs) and the visual associations between observed and predicted case counts. The highest performing model had excellent predictive ability in some states, but performance varied across states. The highest performing models were for Lyme disease search terms, which indicates the high specificity of search terms. We outline challenges of using Google Trends data, including data availability and a mismatch between geographic units. We discuss opportunities for Google Trends data for One Health research, including prediction of additional zoonotic diseases and incorporating environmental and companion animal data. Lastly, we recommend that Google Trends be explored as an option for predicting other zoonotic diseases and incorporate other data streams that may improve predictive performance. [ABSTRACT FROM AUTHOR]

BackgroundThe epidemiology of mental health disorders has important theoretical and practical implications for health care service and planning. The recent increase in big data storage and subsequent development of analytical tools suggest that mining search databases may yield important trends on mental health, which can be used to support existing population health studies.ObjectiveThis study aimed to map depression search intent in the United States based on internet-based mental health queries.MethodsWeekly data on mental health searches were extracted from Google Trends for an 11-year period (2010-2021) and separated by US state for the following terms: "feeling sad," "depressed," "depression," "empty," "insomnia," "fatigue," "guilty," "feeling guilty," and "suicide." Multivariable regression models were created based on geographic and environmental factors and normalized to the following control terms: "sports," "news," "google," "youtube," "facebook," and "netflix." Heat maps of population depression were generated based on search intent.ResultsDepression search intent grew 67% from January 2010 to March 2021. Depression search intent showed significant seasonal patterns with peak intensity during winter (adjusted P

Google Trends has for over a decade been used by researchers in medicine and the social sciences who want to use information about internet searches to gain new data and insights concerning medical and social issues. A similar tool by the same company, Google Flu Trends, was abolished by Google in 2015 due to serious problems with accuracy; raising larger questions about the quality of the data provided, not only by Google, but by all platforms collecting big data. In this article, I use an unplanned experiment to test the reliability and replicability of Google Trends. The results strongly indicate that scientists in all fields should refrain from using the tool Google Trends when conducting research. [ABSTRACT FROM AUTHOR]

Copyright of Revista Perspectiva Empresarial is the property of Fundacion Universitaria Ceipa and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mobile game metrics have received attention since the emergence of big data technology and data-based decision-making. Among different metrics, the monthly active user number is usually significant because it shows the level of players' engagement and the profit of this game as a business. Therefore, the monthly active user number is valuable for researchers, analysts, and decision-makers interested in the mobile game industry. However, the actual monthly active user number data typically have the accuracy, accessibility, granularity, and cost problems. Therefore, a proxy to the monthly active user number would be helpful to facilitate the decision-making process. This paper proposes to capture user activity through the searches on the Internet from an information-seeking perspective. And the online search volume, wiki page view, social media posts and views are proposed as potential proxies. This paper proposes that the online search volume is an acceptable proxy for the monthly active user number in the context of Geo Augmented Reality (AR) mobile games through data analysis. [ABSTRACT FROM AUTHOR]

Big search data offers the opportunity to identify new and potentially real-time measures and predictors of important political, geographic, social, cultural, economic, and epidemiological phenomena, measures that might serve an important role as leading indicators in forecasts and nowcasts. However, it also presents vast new risks that scientists or the public will identify meaningless and totally spurious ‘relationships’ between variables. This study is the first to quantify that risk in the context of search data. We find that spurious correlations arise at exceptionally high frequencies among probability distributions examined for random variables based upon gamma (1, 1) and Gaussian random walk distributions. Quantifying these spurious correlations and their likely magnitude for various distributions has value for several reasons. First, analysts can make progress toward accurate inference. Second, they can avoid unwarranted credulity. Third, they can demand appropriate disclosure from the study authors.

Background: Influenza-like illness (ILI) encompasses symptoms similar to influenza, affecting population health. Surveillance, including Google Trends (GT), offers insights into epidemic patterns. Methods: This study used multiple regression models to analyze the correlation between ILI incidents, GT keyword searches, and climate variables during influenza outbreaks. It compared the predictive capabilities of time-series and deep learning models against ILI emergency incidents. Results: The GT searches for “fever” and “cough” were significantly associated with ILI cases (p < 0.05). Temperature had a more substantial impact on ILI incidence than humidity. Among the tested models, ARIMA provided the best predictive power. Conclusions: GT and climate data can forecast ILI trends, aiding governmental decision making. Temperature is a crucial predictor, and ARIMA models excel in forecasting ILI incidences.

Copyright of Revista de Economía is the property of Universidade Federal do Parana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: This study proposes a novel method for identifying international accounting differences under International Financial Reporting Standards (IFRS). Using Google Trends data extracted between January 2014 and August 2022, it creates an index, the Global IFRS/IAS Search Index (GISI), which comprises the search activities of 121 jurisdictions for 45 IFRS accounting standards. To assess its relative validity, I classify Nobes' (1983) 14 jurisdictions in addition to 20 OECD countries. The cluster analysis demonstrates that the GISI is a viable alternative for analyzing international differences under IFRS. The results indicate that incorporating big data could be beneficial for examining global accounting issues. A judgmental international classification of financial reporting practices [ABSTRACT FROM AUTHOR]

Big search data offers the opportunity to identify new and potentially real-time measures and predictors of important political, geographic, social, cultural, economic, and epidemiological phenomena, measures that might serve an important role as leading indicators in forecasts and nowcasts. However, it also presents vast new risks that scientists or the public will identify meaningless and totally spurious 'relationships' between variables. This study is the first to quantify that risk in the context of search data. We find that spurious correlations arise at exceptionally high frequencies among probability distributions examined for random variables based upon gamma (1, 1) and Gaussian random walk distributions. Quantifying these spurious correlations and their likely magnitude for various distributions has value for several reasons. First, analysts can make progress toward accurate inference. Second, they can avoid unwarranted credulity. Third, they can demand appropriate disclosure from the study authors. [ABSTRACT FROM AUTHOR]

Background and Aims Plants underpin life on Earth and are essential to human existence. Alarmingly, almost 40% of plant species are under threat of extinction, with plants that are not directly useful to humans being particularly vulnerable. Plant diversity and its untapped resources require urgent protection to safeguard our future, but conservation initiatives are biased towards mammals and birds. Plant awareness disparity, formerly known as plant blindness, describes our tendency to ignore plant life and has been suggested to play a crucial role in the bias against funding and support for plant conservation programmes. Previous studies indicate that nature documentaries can generate shifts in audience awareness of animal species by providing vicarious connections to nature. Here, we investigated whether the plant-focused popular BBC show Green Planet had a similar effect for plants and stimulated audience engagement for information after the broadcast. Methods Online searches for further information were considered a form of engagement for evaluation of the interest of the audience in plants portrayed in Green Planet episodes. The big data activities (Google search engine and Wikipedia pageviews trends) related to the plants mentioned in Green Planet episodes were examined over the period covering the broadcast of the show in UK. Key Results Analyses indicate that Green Planet generated increased awareness and stimulated audience engagement for further information about plants featured in the show, with audience reaction driven by the screen time. Conclusions Natural history films can promote plant awareness, and culturomic tools can be used to assess their impact on the general public, potentially also to inform plant conservation strategies. These are promising findings as we strive to increase public awareness of the value of plant life. [ABSTRACT FROM AUTHOR]

During virus outbreaks in the recent past, web behavior mining, modeling, and analysis have served as means to examine, explore, interpret, assess, and forecast the worldwide perception, readiness, reactions, and response linked to these virus outbreaks. The recent outbreak of the Marburg Virus disease (MVD), the high fatality rate of MVD, and the conspiracy theory linking the FEMA alert signal in the United States on 4 October 2023 with MVD and a zombie outbreak, resulted in a diverse range of reactions in the general public which has transpired in a surge in web behavior in this context. This resulted in “Marburg Virus” featuring in the list of the top trending topics on Twitter on 3 October 2023, and “Emergency Alert System” and “Zombie” featuring in the list of top trending topics on Twitter on 4 October 2023. No prior work in this field has mined and analyzed the emerging trends in web behavior in this context. The work presented in this paper aims to address this research gap and makes multiple scientific contributions to this field. First, it presents the results of performing time-series forecasting of the search interests related to MVD emerging from 216 different regions on a global scale using ARIMA, LSTM, and Autocorrelation. The results of this analysis present the optimal model for forecasting web behavior related to MVD in each of these regions. Second, the correlation between search interests related to MVD and search interests related to zombies was investigated. The findings show that there were several regions where there was a statistically significant correlation between MVD-related searches and zombie-related searches on Google on 4 October 2023. Finally, the correlation between zombie-related searches in the United States and other regions was investigated. This analysis helped to identify those regions where this correlation was statistically significant.

Google Trends data can be informative for zoonotic disease incidences, including Lyme disease. However, the use of Google Trends for predictive purposes is underutilized. In this study, we demonstrate the potential to use Google Trends for zoonotic disease prediction by predicting monthly state-level Lyme disease case counts in the United States. We requested Lyme disease data for the years 2010–2021. We downloaded Google Trends search data on terms for Lyme disease, symptoms of Lyme disease, and diseases with similar symptoms to Lyme disease. For each search term, we built an expanding window negative binomial model that adjusted for seasonal differences using a lag term. Performance was measured by Root Mean Squared Errors (RMSEs) and the visual associations between observed and predicted case counts. The highest performing model had excellent predictive ability in some states, but performance varied across states. The highest performing models were for Lyme disease search terms, which indicates the high specificity of search terms. We outline challenges of using Google Trends data, including data availability and a mismatch between geographic units. We discuss opportunities for Google Trends data for One Health research, including prediction of additional zoonotic diseases and incorporating environmental and companion animal data. Lastly, we recommend that Google Trends be explored as an option for predicting other zoonotic diseases and incorporate other data streams that may improve predictive performance.

I describe a brief summary of the development of databases used in accounting research and discuss the research questions addressed in traditional databases and ‘new’ databases. The new data include online searches such as Google Trends data; textual data from corporate disclosures, analyst reports, conference call transcripts, earnings press releases, and news media articles; social network and social media data from Twitter, LinkedIn, Glassdoor, and other data. New data holds promise for research on attention or cognitive processing constraints; on tone/valence, affect, deceptiveness and credibility for capital market and financial reporting outcomes. I examine the econometric challenges of new data and suggest the potential for new data to offer new auditing tools to detect poor financial reporting, which will help to discourage earnings management.

I describe a brief summary of the development of databases used in accounting research and discuss the research questions addressed in traditional databases and ‘new’ databases. The new data include online searches such as Google Trends data; textual data from corporate disclosures, analyst reports, conference call transcripts, earnings press releases, and news media articles; social network and social media data from Twitter, LinkedIn, Glassdoor, and other data. New data holds promise for research on attention or cognitive processing constraints; on tone/valence, affect, deceptiveness and credibility for capital market and financial reporting outcomes. I examine the econometric challenges of new data and suggest the potential for new data to offer new auditing tools to detect poor financial reporting, which will help to discourage earnings management.

In this article has been explored the attitude of Internet users toward the Islamic financial system using data from Google Trends and WorldData.AI. An attitudes towards Islamic finance, Islamic economics and Islamic banking have been compared using from Google Trends survey statistics. Also, in the WorldData. AI. source has been analyzed that the news texts on Islamic finance for the last 12 months and found that the public attitude towards Islamic finance was positive.

Most social phenomena are inherently complex and hard to measure, often due to under-reporting, stigma, social desirability bias, and rapidly changing external circumstances. This is for instance the case of Intimate Partner Violence (IPV), a highly-prevalent social phenomenon which has drastically risen in the wake of the COVID-19 pandemic. This paper explores whether big data—an increasingly common tool to track, nowcast, and forecast social phenomena in close-to-real time—might help track and understand IPV dynamics. We leverage online data from Google Trends to explore whether online searches might help reach "hard-to-reach" populations such as victims of IPV using Italy as a case-study. We ask the following questions: Can digital traces help predict instances of IPV—both potential threat and actual violent cases—in Italy? Is their predictive power weaker or stronger in the aftermath of crises such as COVID-19? Our results suggest that online searches using selected keywords measuring different facets of IPV are a powerful tool to track potential threats of IPV before and during global-level crises such as the current COVID-19 pandemic, with stronger predictive power post outbreaks. Conversely, online searches help predict actual violence only in post-outbreak scenarios. Our findings, validated by a Facebook survey, also highlight the important role that socioeconomic status (SES) plays in shaping online search behavior, thus shedding new light on the role played by third-level digital divides in determining the predictive power of digital traces. More specifically, they suggest that forecasting might be more reliable among high-SES population strata. [ABSTRACT FROM AUTHOR]

The rapid development of information technologies has led to the formation of a "big data" network by increasing the coverage area of the internet network. The use of big data has gained importance in many fields from science to industry, from the private sector to the public. The use of this big data makes the users of the data advantageous compared to their competitors. The aim of this study is to examine the causality relationship between the data obtained from Google Trends, which presents the search frequency of the keyword searched in the Google search engine and the course of gold prices. The data set of the study includes monthly data on the search frequency of the keyword "gold" in the Google search engine within the borders of Turkey between 01.01.2004 and 01.12.2021. In the study, the stationarity of the variables was analyzed, and then the Johansen Cointegration test was used to determine the long-term relationship between the variables. At the last stage, Granger Causality Analysis was performed. As a result of the study, one-way Granger causality towards gold prospecting was determined. [ABSTRACT FROM AUTHOR]

Infodemiologic methods could be used to enhance modeling infectious diseases. It is of interest to verify the utility of these methods using a Nigerian case study. We used Google Trends data to track COVID-19 incidences and assessed whether they could complement traditional data based solely on reported case numbers. Data on the Nigerian weekly COVID-19 cases spanning through March 1, 2020, to May 31, 2021, were matched with internet search data from Google Trends. The reported weekly incidence numbers and the GT data were split into training and testing sets. ARIMA models were fitted to describe reported weekly COVID cases using the training set. Several COVID-related search terms were theoretically and empirically assessed for initial screening. The utilized Google Trends (GT) variable was added to the ARIMA model as a regressor. Model forecasts, both with and without GTD, were compared with weekly cases in the test set over 13 weeks. Forecast accuracies were compared visually and using RMSE (root mean square error) and MAE (mean average error). Statistical significance of the difference in predictions was determined with the two-sided Diebold-Mariano test. Preliminary results of contemporaneous correlations between COVID-related search terms and weekly COVID cases reveal “loss of smell,” “loss of taste,” “fever” (in order of magnitude) as significantly associated with the official cases. Predictions of the ARIMA model using solely reported case numbers resulted in an RMSE (root mean squared error) of 411.4 and mean absolute error (MAE) of 354.9. The GT expanded model achieved better forecasting accuracy (RMSE: 388.7 and MAE = 340.1). Corrected Akaike Information Criteria also favored the GT expanded model (869.4 vs. 872.2). The difference in predictive performances was significant when using a two-sided Diebold-Mariano test (DM = 6.75, p < 0.001) for the 13 weeks. Google trends data enhanced the predictive ability of a traditionally based model and should be considered a suitable method to enhance infectious disease modeling.

Google Trends (GT) offers a historical database of global internet searches with the potential to complement conventional records of environmental hazards, especially in regions where formal hydrometeorological data are scarce. We evaluate the extent to which GT can discern heavy rainfall and floods in Kenya and Uganda during the period 2014 to 2018. We triangulate counts of flood searches from GT with available rainfall records and media reports to build an inventory of extreme events. The Spearman rank correlation (ρ) between monthly mean search interest for flooding and monthly Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) rainfall totals was ρ = +0.38 (p < 0.005) for Kenya and ρ = +0.64 (p < 0.001) for Uganda. Media reports of flooding were used to specify a threshold of detectability to give the same overall frequency of floods based on GT search interest. When the GT search index threshold was set at ≥15 and ≥29, the correct detection rate was 75% and 64% within a five‐day window of known flood events in Kenya and Uganda, respectively. From these preliminary explorations we conclude that GT has potential as a proxy data source, but greater skill may emerge in places with larger search volumes and by linking to historical information about environmental hazards at sub‐national scales. Wider applicability of the GT platform might be possible if there is greater transparency about how Google algorithms determine topics. [ABSTRACT FROM AUTHOR]

California has a high seismic hazard, as many historical and recent earthquakes remind us. To deal with potential future damaging earthquakes, a voluntary insurance system for residential properties is in force in the state. However, the insurance penetration rate is quite low. Bearing this in mind, the aim of this article is to ascertain whether Big Data can provide policymakers and stakeholders with useful information in view of future action plans on earthquake coverage. Therefore, we extracted and analyzed the online search interest in earthquake insurance over time (2004–2021) through Google Trends (GT), a website that explores the popularity of top search queries in Google Search across various regions and languages. We found that (1) the triggering of online searches stems primarily from the occurrence of earthquakes in California and neighboring areas as well as oversea regions, thus suggesting that the interest of users was guided by both direct and vicarious earthquake experiences. However, other natural hazards also come to people's notice; (2) the length of the higher level of online attention spans from one day to one week, depending on the magnitude of the earthquakes, the place where they occur, the temporal proximity of other natural hazards, and so on; (3) users interested in earthquake insurance are also attentive to knowing the features of the policies, among which are first the price of coverage, and then their worth and practical benefits; (4) online interest in the time span analyzed fits fairly well with the real insurance policy underwritings recorded over the years. Based on the research outcomes, we can propose the establishment of an observatory to monitor the online behavior that is suitable for supporting well-timed and geographically targeted information and communication action plans. [ABSTRACT FROM AUTHOR]

Abstract Objective(s) Climate variables are implied in the pathogenesis of certain otologic diseases, including benign paroxysmal positional vertigo (BPPV). Using internet search data obtained through Google Trends (GT), we explored the relationship between climate patterns and symptom search frequencies for BPPV. We hypothesized that increased latitude, as a proxy for decreased sunlight exposure, would lead to increase in BPPV symptom searches. Methods GT searches for symptoms related to BPPV were obtained for five U.S. cities of different latitudes via the Google Trends online interface. Comparisons were made using SPSS via ANOVA analysis. Figures were made using Microsoft Excel. Results Searches for BPPV‐related symptoms increased with increasing latitude. BPPV‐related symptoms did show seasonal variations, but not in predictable manners. Conclusion(s) GT may be a viable research tool when comparing geographical differences in searches, but may be less sensitive in detecting time dependent changes. We offer suggestions as to how big data tools may be altered for research purposes. Level of Evidence NA.

Various branches of the social sciences often require high-frequency measures of collective behavior. Examining peoples' Google search patterns enables uncovering otherwise hard-to-measure social trends or constructing different leading indicators. Google Trends (GT) data presents a unique and rich dataset that enables a deeper look at the popularity of peoples' search interests. This paper delves into the main empirical approaches of constructing GT indicators, conducts a thorough methodological review, and elucidates the nuances and potentials of GT data. Specifically, the paper furnishes a step-by-step methodological guide for constructing GT indicators: from data pre-treatment to composite estimation using an array of machine learning techniques (principal component analysis, XGBoost, and dynamic factor models). We scrutinize the prevalent construction strategies of GT indicators, compare the use of keyword-based vs. category-based estimations, single indicators vs. composite indicators, and evaluate the significance of various data transformations. Illustrating the utility of our methodological framework through a case study, we create GT indicators for US retail trade and examine their forecasting accuracy in comparison to a benchmark autoregressive model. Our findings reveal that XGBoost yields the optimal test sample fit among composite indicators, while category-specific indicators like Shopping and Retail Trade exhibit worse predictive accuracy. As an additional contribution, we provide a detailed and user-friendly R code for replicating our analysis. • Google Trends (GT) data aid in innovating the social science indicators construction. • We build a step-by-step methodological guide to construct GT-based indicators. • Our case study develops and examines GT-based indicators for the US retail trade. • XGBoost provides the best test sample fit of all used machine learning techniques. • Described approach and provided R codes enable wide applicability in social sciences. [ABSTRACT FROM AUTHOR]

Google search is ubiquitous, and Google Trends (GT) is a potentially useful access point for big data on many topics the world over. We propose a new 'variance-in-time' method for forecasting events using GT. By collecting multiple and overlapping samples of GT data over time, our algorithm leverages variation both in the mean and the variance of a search term in order to accommodate some idiosyncracies in the GT platform. To elucidate our approach, we use it to forecast protests in the United States. We use data from the Crowd Counting Consortium between 2017 and 2019 to build a sample of true protest events as well as a synthetic control group where no protests occurred. The model's out-of-sample forecasts predict protests with higher accuracy than extant work using structural predictors, high frequency event data, or other sources of big data such as Twitter. Our results provide new insights into work specifically on political protests, while providing a general approach to GT that should be useful to researchers of many important, if rare, phenomena. [ABSTRACT FROM AUTHOR]

We forecast the US oil consumption level taking advantage of google trends. The google trends are the search volumes of the specific search terms that people search on google. We focus on whether proper selection of google trend terms leads to an improvement in forecast performance for oil consumption. As the forecast models, we consider the least absolute shrinkage and selection operator (LASSO) regression and the structured regularization method for large vector autoregressive (VAR-L) model of Nicholson et al. (2017), which select automatically the google trend terms and the lags of the predictors. An out-of-sample forecast comparison reveals that reducing the high dimensional google trend data set to a low-dimensional data set by the LASSO and the VAR-L models produces better forecast performance for oil consumption compared to the frequently-used forecast models such as the autoregressive model, the autoregressive distributed lag model and the vector error correction model. [ABSTRACT FROM AUTHOR]

The methodological approach for assessing the formation of food demand in unforeseen situations using digital Internet-technologies and the assessment itself, is substantiated in the paper (in the context of the COVID-19 pandemic of 2020). Comparison and theoretical generalization, as well as statistical test-assessment of hypotheses and structural regularities based on the data of Google Trends Internet platform, is used to analyze consumer preferences and intensity of demand changes for meat, milk, sugar, bread, and flour during the pandemic and quarantine, both in developed and developing countries. It is discovered that the biggest changes can be observed in the developed countries: consumer preferences shifted from rather expensive food products (milk and meat) to much cheaper ones (flour and bread). It is asserted that a decrease in consumer demand for basic food products will have a negative impact on the global economy. In 2020, a considerable decrease in GDP is expected for the developed countries; in the developing countries, GDP decline will not be as large, but prices are expected to rise much more noticeably. The following anti-crisis measures are proposed: support of the most vulnerable population and increase of food accessibility; temporary reduction of the VAT and other taxes influencing the price of food; reduction of central banks’ lending rates, etc. With the correct measures applied, the stabilization of consumer demand for food and gradual growth of the global economy is expected by the end of 2021.

Abstract During the last decade, the use of online search traffic data is becoming popular in examining, analyzing, and predicting human behavior, with Google Trends being a popular tool in monitoring and analyzing the users' online search patterns in several research areas, like health, medicine, politics, economics, and finance. Towards the direction of exploring the Sterling Pound’s predictability, we employ Google Trends data from the last 5 years (March 1st, 2015 to February 29th, 2020) and perform predictability analysis on the Pound’s exchange rates to Euro and Dollar. The period selected includes the 2016 UK referendum as well as the actual Brexit day (January 31st, 2020), with the analysis aiming at analyzing the Pound’s relationships with Google query data on Pound-related keywords and topics. A quantile dependence method is employed, i.e., cross-quantilograms, to test for directional predictability from Google Trends data to the Pound’s exchange rates for lags from zero to 30 (in weeks). The results indicate that statistically significant quantile dependencies exist between Google query data and the Pound’s exchange rates, which point to the direction of one of the main implications in this field, that is to examine whether the movements in one economic variable can cause reactions in other economic variables.

Background This study aims to understand whether there is a seasonal change in the internet search interest for Toxoplasma by using the data derived from Google Trends (GT). Methods The present study searched for the relative search volume (RSV) for the search term ' Toxoplasma ' in GT within six major English-speaking countries (Australia, New Zealand [Southern Hemisphere] and Canada, Ireland, the UK and the USA [Northern Hemisphere] from 1 January 2004 to 31 December 2019, utilizing the category of 'health'. Data regarding the RSV of Toxoplasma was obtained and further statistical analysis was performed in R software using the 'season' package. Results There were significantly seasonal patterns for the RSV of the search term ' Toxoplasma ' in five countries (all p<0.05), except for the UK. A peak in December–March and a trough in July–September (Canada, Ireland, the UK and the USA) were observed, while a peak in June/August and a trough in December/February (Australia, New Zealand) were also found. Moreover, the presence of seasonal patterns regarding RSV for ' Toxoplasma ' between the Southern and Northern Hemispheres was also found (both p < 0.05), with a reversed meteorological month. Conclusions Overall, our study revealed the seasonal variation for Toxoplasma in using internet search data from GT, providing additional evidence on seasonal patterns in Toxoplasma. [ABSTRACT FROM AUTHOR]

Evolving tastes can change the relative importance of product features in shaping consumers' purchase decisions, which in turn can shift the relative attractiveness of products with different feature levels. The challenge lies in finding a reliable yet cost-effective way to monitor the weights consumers place on various product features. In the context of the U.S. automotive market, the authors explore the potential of using trends in online searches for feature-related keywords as indicators of trends in the relative importance of the corresponding features (e.g., fuel economy, acceleration, cost to buy, cost to operate, body type). By augmenting marketing-mix data with feature search data in a market response model, they show substantial improvements in goodness-of-fit both in and out of sample. The authors find empirical support for the hypothesis that feature search trends are positively correlated with feature importance trends. They discuss how managers may make better decisions by monitoring feature search trends and leveraging those trends strategically. [ABSTRACT FROM AUTHOR]

The media and election campaign managers conduct several polls in the days leading up to the presidential elections. These preelection polls have a different predictive capacity, despite the fact that under a Big Data approach, sources that indicate voting intention can be found. In this article, we propose a free method to anticipate the winner of the presidential election based on this approach. To demonstrate the predictive capacity of this method, we conducted the study for two countries: the United States of America and Canada. To this end, we analysed which candidate had the most Google searches in the months leading up to the polling day. In this article, we have taken into account the past four elections in the United States and the past five in Canada, since Google first published its search statistics in 2004. The results show that this method has predicted the real winner in all the elections held since 2004 and highlights that it is necessary to monitor the next elections for the presidency of the United States in November 2020 and to have more accurate information on the future results. [ABSTRACT FROM AUTHOR]