Search

INTRODUCTION: COVID-19 infection was accompanied by otologic symptoms, a pattern that was captured early by Google Trends. The objective of this study is to investigate searches for otologic symptoms and identify correlations with the pandemic onset. MATERIALS AND METHODS: Search interest for otologic symptoms was gathered using Google Trends from two years before and two years following the pandemic start date. A two-tailed Mann-Whitney U test was used to identify significant changes and effect size. RESULTS: In total, search interest for 14 terms was collected, with significant changes identified in 11. Six terms showed increased search interest, with the most significant rises observed for headache (r=0.589, p

This article evaluates online social behavior regarding the establishment of limited-use areas (LUAs) around the airports in Warsaw and Gdansk. The study relied mainly on an analysis of Google Trends statistics, in particular the dynamics of keyword searches. The article suggests that assessments of online behavior can provide a deeper understanding of social behavior. The study involved an OLS regression analysis and a causal impact analysis of the intervention based on a Bayesian structural time-series model. This article analyses different phases of an information society's activity, from the introduction of LUAs around airports to the deadline for submitting compensation claims. The results indicate that the number of searches for the keyword "compensation" increased significantly after the introduction of LUAs and that RSV decreased after the end of the compensation process, which confirms that the intervention significantly influenced the analyzed time series. [ABSTRACT FROM AUTHOR]

Indicators are important tools for tracking the socio‐environmental impacts of droughts and building resilience to climate change. We begin with an overview of metrics used for water planning and drought management, with particular emphasis on the UK. We explain how considerations of cost, immediacy, access, consistency, relevance, reliability and others denote the suitability of information for developing new indicators. We then demonstrate the potential of Google Trends (GT) online search data as drought indicators for England and Ireland. We show that search terms such as 'drought', 'water butt' and 'hosepipe ban' correlate significantly with conventional hydroclimatic data as well as with newspaper reports of various drought impacts during the period 2011–2022. GT data also show evidence of rising interest in water saving technologies, especially for outdoor water use. Meanwhile, online searches for 'Defra' and 'Environment Agency' have declined and are more often associated with flood episodes than droughts. Interest in water companies in England is more likely around hosepipe bans than water leakage (although this varies by company). We discuss the implications of these findings for targeting information campaigns, plus prospects for monitoring drought impacts and public sentiment in near real‐time. [ABSTRACT FROM AUTHOR]

New fashion product sales forecasting is a challenging problem that involves many business dynamics and cannot be solved by classical forecasting approaches. In this paper, we investigate the effectiveness of systematically probing exogenous knowledge in the form of Google Trends time series and combining it with multi‐modal information related to a brand‐new fashion item, in order to effectively forecast its sales despite the lack of past data. In particular, we propose a neural network‐based approach, where an encoder learns a representation of the exogenous time series, while the decoder forecasts the sales based on the Google Trends encoding and the available visual and metadata information. Our model works in a non‐autoregressive manner, avoiding the compounding effect of large first‐step errors. As a second contribution, we present VISUELLE, a publicly available dataset for the task of new fashion product sales forecasting, containing multimodal information for 5,577 real, new products sold between 2016 and 2019 from Nunalie, an Italian fast‐fashion company. The dataset is equipped with images of products, metadata, related sales, and associated Google Trends. We use VISUELLE to compare our approach against state‐of‐the‐art alternatives and several baselines, showing that our neural network‐based approach is the most accurate in terms of both percentage and absolute error. It is worth noting that the addition of exogenous knowledge boosts the forecasting accuracy by 1.5% in terms of Weighted Absolute Percentage Error (WAPE), revealing the importance of exploiting informative external information. The code and dataset are both available online (at https://github.com/HumaticsLAB/GTM-Transformer). [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated the capacity of Internet search data to be used for monitoring and tracking infectious diseases. Given that the Philippines has one of the most rapidly expanding human immunodeficiency virus (HIV) epidemics in the globe, it is imperative to enhance the local surveillance and prediction of HIV by utilizing easily accessible web data such as Google Trends. This study aims to [1] evaluate the advantages of utilizing Google Trends data (GTD) in predicting new monthly HIV diagnoses in the Western Visayas and [2] determine the suitable modeling approach that effectively utilizes this tool in forecasting HIV cases in the region. Data on new monthly HIV cases from January 2012-December 2022 in Western Visayas was requested from the Department of Health Region VI Office and HIV-related internet search data was obtained from Google Trends. Data imputations were applied to mitigate the effects of COVID-19 on HIV data. The results of the traditional seasonal autoregressive integrated moving average (SARIMA) model were compared with the results of the neural network autoregression (NNAR) model using cross-validation via rolling forecast origin. GTD have a moderately weak but significant association with HIV case numbers in Western Visayas. NNAR models outperformed the traditional SARIMA models in terms of mean absolute percentage error, mean absolute error, and root mean squared error. The use of Google search data in NNAR models improved the predictive accuracy of the forecasts when using moving average imputation as a replacement for outliers. However, this does not accurately represent the overall NNAR process, as the linear interpolation replacements did not yield comparable outcomes. This study showed that, if suitable models are adopted, utilizing HIV-related web data has the potential to produce real-time surveillance systems for HIV monitoring and forecasting. This presents an opportunity to enhance local surveillance in the country not just for HIV but also for other infectious diseases such as tuberculosis and dengue. Internet search data is free and readily available, and public health agencies can adopt this type of modeling, even with limited funding; however, further study is advised. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To study geographic patterns in ophthalmologist supply and patient demand for services in the United States. Google Trends data for the keywords "ophthalmology" and "ophthalmologist" between 2004 and 2019 were queried and normalized to determine relative search volumes (RSV) for each United States state. Ophthalmologist density was calculated by dividing the number of practicing ophthalmologists by the State Census Bureau population estimates. RSV values were divided by ophthalmologist density and normalized to calculate the relative demand index (RDI) for each state. The number of accredited ophthalmology programs per state was acquired through the Accreditation Council for Graduate Medical Education. Ophthalmologist concentration was highly heterogeneous across the country. The states with the highest concentration of ophthalmologist per 10,000 people were Washington, DC (1.42), Maryland (0.94), Massachusetts (0.87), and New York (0.86), while the lowest were Wyoming (0.19), Idaho (0.36), New Mexico (0.38), and Nevada (0.39). RSVs ranged from 36 (Alaska and North Dakota) to 100 (Michigan). The highest RDI was found in South Dakota (100), Delaware (84), Michigan (66), and Arizona (56). The lowest RDI was in Washington, DC (0), Hawaii (7), Oregon (8), and Montana (14). The highest number of ophthalmology residency programs were in New York (18), Texas (9), and California (9), whereas 12 states lacked residency programs altogether. In this study, we found a wide range in the geographic distribution of ophthalmologists and residency programs in the United States. States with the highest relative demand index may represent areas most at risk of unmet medical needs. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Forecasting is the property of Wiley-Blackwell 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.)

Traditional face-to-face education has shifted to online education to prevent large gatherings and crowds from spreading the COVID-19 virus. Several online platforms like Zoom, GoToMeeting, Microsoft Teams, and WebEx restore traditional teaching and promote online education. Online learning classes are particularly beneficial for hospitalized students, massive open online courses (MOOCS), and lifelong learners. This paper uses the deep learning model to predict student performance in an online environment. Student interaction with the online environment is vital to predicting student performance. This prediction will help identify at-risk students, and teachers can help motivate the poor-performance students. We used student interaction features like click sums. We studied credits to understand the students' behaviour and tried to forecast the outcomes of their final scores by using the hybrid deep learning models. The proposed hybrid model predicts student performance with an accuracy of 98.80%. The results proved that the proposed deep learning model effectively predicts student performance in an online environment. [ABSTRACT FROM AUTHOR]

This study systematically reviewed the literature on using the Google Search Volume Index (GSVI) as a proxy variable for investor attention and stock market movements. We analyzed 56 academic studies published between 2010 and 2021 using the Web of Sciences and ScienceDirect databases. The articles were classified and synthesized based on the selection criteria for building the GSVI: keywords of the search term, market region, and frequency of the data sample. Next, we analyze the effect of returns, volatility, and trading volume on the financial variables. The main results can be summarized as follows. (1) The GSVI is positively related to volatility and trading volume regardless of the keyword, market region, or frequency used for the sample. Hence, increasing investor attention toward a specific financial term will increase volatility and trading volume. (2) The GSVI can improve forecasting models for stock market movements. To conclude, this study consolidates, for the first time, the research literature on GSVI, which is highly valuable for academic practitioners in the area. [ABSTRACT FROM AUTHOR]

Objective: The aim of our study is to examine the relationship between the economic activity of small firms and the mental well-being of the population in five Latin American countries in the early stages of the pandemic. Methods: We utilize the search volume of certain keywords on Google Trends (GT), such as "boredom," "frustration," "loneliness," "sleep", "anxiety", and "depression", as an indicator of the well-being of the population. By examining the data from Facebook Business Activity Trends, we investigate how social attention reacts to the activity levels of different economic sectors. Results: Increased business activity is generally associated with reduced levels of boredom, loneliness, sleep problems and anxiety. The effect on depression varies by sector, with positive associations concentrated in onsite jobs. In addition, we observe that strict Non-Pharmaceutical Interventions (NPIs) tend to exacerbate feelings of boredom and loneliness, sleep issues, and anxiety. Conclusions: Our findings suggest a strong association between different indicators of psychological well-being and the level of activity in different sectors of the economy. Given the essential role of small and medium-sized enterprises (SMEs) in generating employment, especially during crises like the pandemic, it is imperative that they remain resilient and adaptable to support economic recovery and job preservation. To accomplish this, policymakers need to focus on providing financial stability and support for SMEs, fostering social support networks within companies, and incorporating mental health services into workplace environments. This comprehensive strategy can alleviate mental health challenges and enhance public health resilience. [ABSTRACT FROM AUTHOR]

It is estimated that the tropical and south temperate regions harbour approximately 85% of the total entomofauna, and Brazil, which is the largest country in the Neotropical region, shows the greatest insect diversity in the world. However, to the best of our knowledge, the Brazilian population's interest in insects is yet to be investigated.We use Google Trends platform for prospecting temporal variation in the public engagement with nature. From searches in this database, from 2012 to 2020, using common names of 21 insect groups, we evaluated: (I) Brazilians' internet interest on insects over time, (II) the highest search themes for each insect group and (III) which pairs of insects are positively or negatively correlated regarding the public interest through years.Our findings demonstrate that Brazilians' internet interest regarding the studied insect groups is constant over time, which may be linked with the low attention these organisms generally receive, and that Brazilians are prone to perceive them as harmful, as we can observe in many of the top queries related to studied groups.We observed a considerable synchronous growth in interest regarding pairs of insects considered as domestic pests, medically important and charismatic, and the searches on these groups referred mainly to their biological, ecological and cultural aspects.Our results indicate that investment in environmental education in Brazil can motivate public engagement with insects. Also, our data can be crucial for directing conservation policies for insects in the tropical region. [ABSTRACT FROM AUTHOR]

Suicide is a significant public health concern globally, with its varying rates influenced by numerous factors, including seasonal changes. Online search behaviors, particularly searches related to suicide and mental health, have been proposed as real-time indicators of suicidal ideation in populations. In this study, a cross-sectional time series analysis was conducted, utilizing data on suicide attempts from the Polish Police Headquarters and online search behavior from Google Trends over a decade. Suicide attempt data were analyzed alongside the frequency of Google searches for suicide-related keywords derived from the Polish Corpus of Suicide Notes. A total of 66 keywords were selected for analysis to identify seasonal trends and patterns in search behavior. The study employed linear regression, Seasonal Mann-Kendall tests, and TBATS models to analyze the data. Suicide rates show seasonal patterns, peaking in warmer months. However, keyword searches did not strongly correlate with peak suicide months. This study enhances our understanding of suicide-related search trends and their potential connection to suicide rates. It suggests avenues for more effective prevention efforts and the potential for future algorithms to predict suicide rates and identify at-risk groups. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 pandemic has led to a global rise in sleep disorders, serving as a critical indicator of societal mental health. This study aims to examine the trends in health information searches related to sleep disorders during the first seven months of the COVID-19 pandemic in Iran, comparing these trends with the same period two years prior. Methods: This descriptive study analyzed the internet search behavior of Iranians seeking health information between 2017 and 2022. Data were collected on sleep disorder-related searches during the initial seven months of the COVID-19 pandemic in Iran using Google Trends. A one-way analysis of variance (ANOVA) was employed to compare the volume and percentage of searches related to sleep disorders during the pandemic with those in the corresponding periods of the previous years. Additionally, Pearson's correlation was used to assess the relationship between the daily trends in COVID-19-related deaths and the volume of internet searches concerning sleep disorders. Results: The volume of searches related to sleep disorders significantly increased (p

Alexander C Lieu,1,2 Benton G Chuter,1,2 Niloofar Radgoudarzi,1,2 Evan H Walker,2 John H Huang,2 Nathan L Scott,2 Natalie A Afshari2 1School of Medicine, University of California San Diego, La Jolla, CA, USA; 2Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, CA, USACorrespondence: Natalie A Afshari, University of California, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, 9415 Campus Point Drive, La Jolla, San Diego, CA, 92037, USA, Tel +1-858-534-6290, Email naafshari@health.ucsd.eduPurpose: To study geographic patterns of supply and demand for uveal melanoma and other ocular oncology healthcare by ocular oncology physicians in the United States.Methods: Google search interest data was obtained through trends.google.com. The combined-state density of ocular oncology physicians was calculated by dividing the number of practicing ocular oncologists in each state and its surrounding states by the state population. Relative search volume (RSV) values were divided by ocular oncology physician density to calculate the Google relative demand index (gRDI) for each state. Medicare (mRDI) and IRIS® Registry (iRDI) relative demand indices were calculated using prevalence data obtained through the Vision and Eye Health Surveillance System (VEHSS). Data from the US Census Bureau and Centers for Disease Control (CDC) databases were also utilized to analyze associations with poverty rates, percent living in urban or rural areas, vision screening rates, and ocular neoplasm rates.Results: Alabama showed the highest RSV (100), while the lowest was reported in New Mexico (20). Vermont had the highest density of combined-state ocular oncology ophthalmologists (1.85 per 100,000 residents). New Mexico had the lowest RDI (0.013 gRDI, 0.015 mRDI, 0.018 iRDI) with 32 combined-state ocular oncologists and a population of 2,114,371. Ocular neoplasm prevalence rates ranged between 1.32% and 5.40% and significantly correlated with RSV. Single-state gRDI correlated with rural status and negatively correlated with urban areas (≥ 50,000 individuals). Single-state ophthalmologist density correlated positively with percent living in urban areas and vision screening rates, and negatively with rural status.Conclusion: This study uncovered significant heterogeneity in the geographical distribution of ocular oncology physicians and RDI throughout the United States, highlighting potential undersupply scenarios. This may guide efforts to increase ocular oncology physician and surgeon availability in areas of need.Keywords: ocular oncology, google trends, medicare, IRIS® Registry, uveal melanoma

The objective of this research was to investigate the information search behavior of Iranians on the Internet during the COVID-19 pandemic and to examine the relationship between the peak of the disease and the searches performed on the Internet. The purpose of this research was fundamental, and the research method was descriptive. The statistical population is the searches performed by Iranians from February 2020 to April 2022, and the investigated sample was the searched concepts related to anxiety and mental health. The first part of the data consisted of the queries that were extracted from the Google Trends website in December 2022. The second part, which consisted of the daily statistics of death and new infections, was obtained from the Johns Hopkins University website. Python version 3.12 was employed to conduct data analysis, which included the Granger causality test and Pearson correlation. The results indicate that the search for anxiety-related concepts has increased at the beginning of the epidemic. The quantity of searches for anxiety concepts at the beginning of the epidemic is positively and significantly correlated with the number of deaths caused by the disease. Consequently, the quantity of anxiety searches should increase over a period of a few days as the number of deaths increases. These searches diminished as the number of deaths decreased; however, the trend of anxiety searches ceased to be correlated with the number of deaths after a few months. The search for psychological treatment was modest at the onset of the epidemic; however, it increased after nine months, and the number of deaths also increased, prompting an increase in these searches. The results show that internet inquiries can be a useful tool for monitoring community concerns.

Abstract This study systematically reviewed the literature on using the Google Search Volume Index (GSVI) as a proxy variable for investor attention and stock market movements. We analyzed 56 academic studies published between 2010 and 2021 using the Web of Sciences and ScienceDirect databases. The articles were classified and synthesized based on the selection criteria for building the GSVI: keywords of the search term, market region, and frequency of the data sample. Next, we analyze the effect of returns, volatility, and trading volume on the financial variables. The main results can be summarized as follows. (1) The GSVI is positively related to volatility and trading volume regardless of the keyword, market region, or frequency used for the sample. Hence, increasing investor attention toward a specific financial term will increase volatility and trading volume. (2) The GSVI can improve forecasting models for stock market movements. To conclude, this study consolidates, for the first time, the research literature on GSVI, which is highly valuable for academic practitioners in the area.

Abstract Objective The aim of our study is to examine the relationship between the economic activity of small firms and the mental well-being of the population in five Latin American countries in the early stages of the pandemic. Methods We utilize the search volume of certain keywords on Google Trends (GT), such as “boredom,” “frustration,” “loneliness,” “sleep”, “anxiety”, and “depression”, as an indicator of the well-being of the population. By examining the data from Facebook Business Activity Trends, we investigate how social attention reacts to the activity levels of different economic sectors. Results Increased business activity is generally associated with reduced levels of boredom, loneliness, sleep problems and anxiety. The effect on depression varies by sector, with positive associations concentrated in onsite jobs. In addition, we observe that strict Non-Pharmaceutical Interventions (NPIs) tend to exacerbate feelings of boredom and loneliness, sleep issues, and anxiety. Conclusions Our findings suggest a strong association between different indicators of psychological well-being and the level of activity in different sectors of the economy. Given the essential role of small and medium-sized enterprises (SMEs) in generating employment, especially during crises like the pandemic, it is imperative that they remain resilient and adaptable to support economic recovery and job preservation. To accomplish this, policymakers need to focus on providing financial stability and support for SMEs, fostering social support networks within companies, and incorporating mental health services into workplace environments. This comprehensive strategy can alleviate mental health challenges and enhance public health resilience.

Purpose: This study tests whether environmental awareness affects firm creation by using Google Trends data and a novel region-level data set from Italy. Design/methodology/approach: Forward-looking entrepreneurs drive firm creation. The authors hypothesize that more environmentally conscious entrepreneurs will emerge as environmental awareness rises, increasing the number of green and energy firms. The authors test the prediction using Google Trends data and a novel region-level data set from Italy. Findings: The authors find that not only the number of green and energy-innovative firms but also that of all innovative start-ups increases with rising environmental consciousness. The results imply some "innovation spillover" effects from green sectors to other industries with rising environmental awareness. Originality/value: The paper hypothesizes that as environmental awareness rises, more environmental-conscious entrepreneurs will emerge, which would increase the number of green and energy firms. Robustness and falsification tests are also offered. [ABSTRACT FROM AUTHOR]

Sugi Panneerselvam, Nina Diklich, Jonathan Tijerina, Michelle M Falcone, Kara M Cavuoto Department of Ophthalmology, Bascom Palmer Eye Institute/University of Miami Miller School of Medicine, Miami, FL, USACorrespondence: Kara M Cavuoto, Department of Ophthalmology, Bascom Palmer Eye Institute/University of Miami Miller School of Medicine, 900 NW 17th Street, Miami, FL, 33136, USA, Tel +1-305-326-6324, Fax +1-305-547-3675, Email kcavuoto@med.miami.eduPurpose: To explore public interest in myopia progression and management and to correlate these trends to available treatments.Methods: Keywords were chosen for interest in myopia overall and those signifying interest in myopia treatments. Treatment options were separated into four main categories: atropine, glasses, contact lenses, and orthokeratology. Search terms were queried across ten years of Google Trends data and the relative search volume was analyzed to quantify the change in search volume over time.Results: A positive linear trend over time was present for all myopia interest keywords except “nearsighted” (p = 0.074) and “near work myopia” (p = 0.086). Interest in the four myopia treatment categories included in this study also displayed a significant positive trend over time. There is also a statistically significant positive correlation between all four treatment options and four of the seven categories of population interest, “myopia control”, “myopia”, “myopia progression”, and “screen time myopia”.Conclusion: This study demonstrates the utility of GT to correlate public interest in myopia treatments over time. All treatment terms had statistically significant linear search volume growth over a ten-year period. The positive correlation between interest in myopia as a health problem and available treatments supports existing evidence that GT can track rising public health concerns and corresponding treatment-seeking behaviors.Keywords: myopia, Google Trends, nearsighted, orthokeratology, atropine

Purpose – The objective of this paper is to examine the determinants of the Greek house market during the period 2006–2022 using not only economic variables but also behavioral variables, taking advantage of available information on the volume of Google searches. In order to quantify the behavioral variables, we implement a Python code using the Pytrends 4.9.2 library. Design/methodology/approach – In our study, we assert that models relying solely on economic variables, such as GDP growth, mortgage interest rates and inflation, may lack precision compared to those that integrate behavioral indicators. Recognizing the importance of behavioral insights, we incorporate Google Trends data as a key behavioral indicator, aiming to enhance our understanding of market dynamics by capturing online interest in Greek real estate through searches related to house prices, sales and related topics. To quantify our behavioral indicators, we utilize a Python code leveraging Pytrends, enabling us to extract relevant queries for global and local searches. We employ the EGARCH(1,1) model on the Greek house price index, testing several macroeconomic variables alongside our Google Trends indexes to explain housing returns. Findings – Our findings show that in some cases the relationship between economic variables, such as inflation and mortgage rates, and house prices is not always consistent with the theory because we should highlight the special conditions of the examined country. The country of our sample, Greece, presents the special case of a country with severe sovereign debt issues, which at the same time has the privilege to have a strong currency and the support and the obligations of being an EU/EMU member. Practical implications – The results suggest that Google Trends can be a valuable tool for academics and practitioners in order to understand what drives house prices. However, further research should be carried out on this topic, for example, causality relationships, to gain deeper insight into the possibilities and limitations of using such tools in analyzing housing market trends. Originality/value – This is the first paper, to the best of our knowledge, that examines the benefits of Google Trends in studying the Greek house market.

Abstract Human relationships with nature may sometimes manifest through fear, disgust and other disease‐avoidance mechanisms. While there is an evolutionary utility to these so‐called ‘biophobias’, many people exhibit phobic responses towards organisms that pose no tangible threats, potentially leading to excessive anxiety and avoidance of interactions with nature. Understanding the drivers of the prevalence and spread of biophobias in modern societies is, therefore, a growing concern. Here, we posit that online information‐seeking patterns may reveal general insights into biophobias. Using a culturomics approach, we gathered temporal (2004–2022) and country‐level data on the volume of internet searches for 25 biophobias, as well as 25 general phobias acting as a benchmark group. We explored temporal trends in the volume of search for each biophobias and modelled relationships between search volume for biophobias and five country‐level variables. We observed a steady increase in online search volume for biophobias between 2004 and 2022. Yet, there were marked differences in individual trends, with 17 biophobias showing positive, three negative and five stationary temporal trends. Arachnophobia (fear of spiders) attracted the most interest, followed by mysophobia (fear of microbes) and parasitophobia (fear of parasites). The United States, the United Kingdom, Australia, Canada, Mexico and India recorded wide interest in most biophobias, whereas 49% of countries showed no search volume for any biophobia. Search patterns for biophobias were strongly associated with the percentage of urban population, urban population growth and the number of extant venomous species in a given country. Conversely, search patterns for biophobias were weakly correlated with the incidence of anxiety disorders in a country's population and the likelihood of encounters with venomous animals. Our results provide quantitative support to the hypothesis that biophobias are broadly prevalent and possibly increasing as a result of widespread urbanisation and loss of experiences with nature. We suggest that people affected by biophobic disorders may be using the Internet as a key venue to seek relevant information to appraise their condition and identify coping mechanisms. These findings have broad ramifications for understanding and mitigating human–wildlife conflicts and the prevalence of widespread biophobic sentiments in modern societies.

Urology is one of the most competitive specialties in medicine, creating a challenge for prospective students looking to secure a residency position. Our study aims to assess online interest in urology residency by querying online interaction with search terms and criteria for urology residency programs. Utilizing Google Trends analysis from 2011 to 2024, this study examined urology-related search volume indexes, as well as temporal and geographical patterns. Furthermore, the number of residency positions from the American Urological Association database for the 2022 match process was evaluated. Our analysis of temporal trends revealed increased interest in urologist salaries from 2011 to 2019, followed by a decline from 2019 to 2023. Interest in urology-related interviews, applications, research, and letters increased in 2019, marked by the start of the COVID-19 pandemic. California, New York, and Texas had the lowest interest-to-position (IP) ratio, while Maryland, New Jersey, and Virginia had the highest IP ratio. Our analysis reveals an evolving interest in salaries, residency programs, and USMLE Step 1 changes in areas connected with urology. We report key geographical areas with high urology residency interest and low numbers of programs, implying a need for expanded residencies in underserved yet high-interest areas. Awareness and continued interest monitoring after the COVID-19 pandemic is critical for understanding interest in urology applicants.

Introduction:COVID-19 infection was accompanied by otologic symptoms, a pattern that was captured early by Google Trends. The objective of this study is to investigate searches for otologic symptoms and identify correlations with the pandemic onset.Materials and Methods:Search interest for otologic symptoms was gathered using Google Trends from two years before and two years following the pandemic start date. A two-tailed Mann-Whitney U test was used to identify significant changes and effect size.Results:In total, search interest for 14 terms was collected, with significant changes identified in 11. Six terms showed increased search interest, with the most significant rises observed for headache (r=0.589, p

Google Trends, despite its controversial nature for some authors, can be considered an illustrative tool in exploring the political inclinations of a given audience. In the current European Union context, understanding the views and opinions of the public is of paramount importance. Through the analysis of search trends, Google Trends can provide valuable insights into the popularity of political parties in the context of the European Union along with other jurisdictions and how these trends change over time. Furthermore, by incorporating fractal dimensions and ARFIMA (Autoregressive Fractionally Integrated Moving Average) analysis into the data obtained, it is possible to reveal previously non-evident relationships, thereby providing a more comprehensive understanding of the audience‘s political leanings and their interest in specific political parties. The aim of this exploratory study is to assess the potential of ARFIMA, applied to Google Trends data, in characterizing political parties. Preliminary results indicate that this apparatus can be useful for that purpose.

The rapid development of digital technologies, digital transformation, business innovation, and the emergence of such technologies as artificial intelligence, blockchain, the Internet of Things provides significant potential for transforming business processes and creating new innovative models. The trend analysis conducted in this article (using the Google Trends tool) around the world for 2009-2023 showed that public interest in “business innovation” was almost constant until 2021 (according to the GT Scale, it ranged from 0.25 to 0.5). After 2021, it increased (according to the GT Scale, it was more than 0.5). At the same time, public interest in “digital transformation” has significantly increased since 2014, reaching almost maximum values in 2020-2023 (0.75 - 1.00 on the GT Scale). This confirms a growing need in society to acquire new knowledge related to the ability of businesses to adapt to digital transformation, including through creating innovative developments. Unlike trend analysis, bibliometric and comparative analyses identify trends in developing interest in this topic not among ordinary citizens - users of the Google search platform ‒ but among scientists who publish the results of their research either in monographs or in authoritative peer-reviewed scientific journals indexed by leading scientometric databases. The comparative analysis conducted in the article using the Google Books Ngram Viewer (Google) tool, which displays the frequency of references to relevant terms in English-language books for 2009-2019 worldwide, showed similar trends: the persistence of scholars' attention to business innovation, while their interest in digital transformation is growing: in 2009-2013 - a slight increase, in 2013-2019 ‒ a significant increase. In the bibliometric analysis, a five-level mechanism for limiting the research sample was applied: only English-language and most recent (for 2018-2022) scientific articles indexed by the Scopus database in the subject area of Business, Management and Accounting were subject to analysis. Based on these limitations, the research sample for the keyword “digital transformation” was reduced from 12131 to 1700 papers, and for “business innovation” from 865 to 70. The analysis of the published articles, their views and citations, carried out using the SciVal tool, fully confirmed the conclusions drawn from the trend and comparative analysis results. In terms of digital transformation, the leaders in terms of the number of papers and the number of views are Germany, Italy, the UK and the US; in terms of citations - the UK, Germany, the US, and in terms of individual scientists - Brazil and Germany. As for the topic of business innovation, the United States is the undisputed leader in all these parameters, followed by China and the United Kingdom. Some scientists from Germany and Spain also attract the attention of the academic community (in terms of views and citations). Changes in trends and research priorities in each of the studied topics were identified and described using SciVal (analysis of the reference dynamics to the top 50 keywords in articles for 2018-2023). For example, in 2023, compared to 2022, scientific interest in issues related to “value creation” (+61.5%), "incumbents" (+66.7%), “service economy” (+83.3%), “digital marketing” (+75.0%), and “design thinking” (+150.0%) increased the most. VOSviewer software made it possible to cluster the scientific work by thematic areas. The largest cluster on digital transformation relates this concept to small and medium-sized businesses and technologies, and the largest cluster on business innovation relates it to the development of small and medium-sized enterprises and the protection of property rights.

The concept of sustainable smart urban development is an important element of the modern discourse on the future development of cities, regions, and the world as a whole, a key component of a strategy aimed at introducing innovations, improving the life quality of citizens, ensuring inclusiveness and sustainability of local development. It integrates the concept of Smart City as the application of advanced innovative technologies to solve urban problems and the concept of Sustainable Development as a balancing of socio-economic and environmental interests to achieve the Sustainable Development Goals. While discussing the problems and prospects introducing any social models or strategies, it is important to understand the level of public interest in this issue, the level of citizens’ awareness, the readiness to receive new information, and the readiness for change. The article is focused on the analysis of retrospective dynamics (from 01.01.2010 to 01.12.2023) and economic and mathematical modelling of forecast trends in changing public interest in the concepts of “Smart City” and “Sustainable Development” (identified using the Google Trends tool). The analysis was carried out for the world as a whole and for Ukraine specifically. The study identifies and describes the evolution of changes in public interest in these topics, identifies the time points of a sharp surge in interest, and conducts a comparative analysis in the content (trends for these two search queries) and spatial context (trends in Ukraine compared to global trends). The countries of the world and regions of Ukraine where user interest in the relevant topics dominates are ranked, and the countries of the world and regions of Ukraine where user interest in the relevant topics dominates are ranked, the TOP-10 are identified. The factors whose influence is relevant to the public interest dynamics are identified (consumer trends, media activity, dynamics of the introduction of relevant concepts, cultural, linguistic, and historical contexts, the general level of education, digital inclusion and consciousness of the population of different countries, etc.) Using the exponential smoothing method, models were built to predict the dynamics of time series – the number of queries from Ukrainian Internet users regarding the concepts of “Smart City” and “Sustainable Development”. Additive forecasting models, which differ in the trend component, are obtained.

Abstract Measles remains a significant threat to children worldwide despite the availability of effective vaccines. The COVID-19 pandemic exacerbated the situation by leading to the postponement of supplementary measles immunization activities. Along with this postponement, measles surveillance also deteriorated, with the lowest number of submitted specimens in over a decade. In this study, we focus on measles as a challenging case study due to its high vaccination coverage, which leads to smaller outbreaks and potentially weaker signals on Google Trends. Our research aimed to explore the feasibility of using Google Trends for real-time monitoring of infectious disease outbreaks. We evaluated the correlation between Google Trends searches and clinical case data using the Pearson correlation coefficient and Spearman’s rank correlation coefficient across 30 European countries and Japan. The results revealed that Google Trends was most suitable for monitoring acute disease outbreaks at the regional level in high-income countries, even when there are only a few weekly cases. For example, from 2017 to 2019, the Pearson correlation coefficient was 0.86 (p-value< 0.05) at the prefecture level for Okinawa, Japan, versus 0.33 (p-value< 0.05) at the national level for Japan. Furthermore, we found that the Pearson correlation coefficient may be more suitable than Spearman’s rank correlation coefficient for evaluating the correlations between Google Trends search data and clinical case data. This study highlighted the potential of utilizing Google Trends as a valuable tool for timely public health interventions to respond to infectious disease outbreaks, even in the context of diseases with high vaccine coverage.

Since its public introduction in 2009, Bitcoin has grown to be the most well-known cryptocurrency worldwide. There is still debate as to whether Bitcoin may be used as a hedge against other assets. The purpose of this study is to investigate the correlation between Bitcoin and conventional commodity markets such as gold, crude oil, stock markets, and investor interest (quantified via Google Trends). In addition, the paper also tests Bitcoin’s safe haven role compared to other commodity markets. The Vector Autoregression model using daily database collected during the period 2013–2021 is employed to investigate the relationship between Bitcoin and traditional commodity markets. The impulse response function is used to analyze Bitcoin price movements against economic shocks from gold, oil prices, and the Dow Jones Industrial Average. In addition, the value-at-risk (VaR) model is used to test Bitcoin’s safe-haven property compared to other conventional commodity markets. The research results show that Bitcoin has negative impacts on gold, crude oil prices, and the stock market. Besides, Bitcoin responds negatively to a sharp decline in investor interest. Furthermore, the results of the VaR model show that Bitcoin is the second most volatile and risky asset, only after the crude oil market, and much riskier than gold. This result proves that Bitcoin cannot yet be considered a safe-haven instrument. These findings have several implications for investors and policymakers to minimize the risks associated with this cryptocurrency. AcknowledgmentThe authors would like to send their sincere thanks to the Reviewers and Editorial Board of the Journal. Their valuable comments and helpful support helped improve the paper’s quality. No funding was granted for this study.

Abstract The semiconductor price index serves as a vital metric for assessing technological developments and related market trends. Establishing a more accurate forecasting model for the semiconductor price index is of significant importance for analyzing the industry’s trends and market directions. In this paper, a novel framework for semiconductor price index forecasting is proposed. In addition to traditional financial data, the study introduces search engine data (Google Trends) representing investor attention, and introduces text information extracted from online news headlines reflecting major market events and government policies as independent variables. Used to predict the dependent variable: The PHLX Semiconductor Sector (SOX). First, the XGBoost model is employed to compute the importance scores of each feature. Then, a feature weight coefficient indicator is constructed based on these importance scores to calculate the weight coefficient indicator values for each feature. These indicator values are then used to weight the kernel function of Support Vector Regression (SVR), resulting in weighted Support Vector Regression (WSVR). Finally, WSVR is utilized as the base learner for Adaptive Boosting (AdaBoost), yielding the XGBoost–WSVR–AdaBoost model based on feature weighting. The proposed model outperforms AdaBoost, RNN, ERT, LSTM, and other models in terms of Mean Absolute Percentage Error (MAPE) and goodness-of-fit ( $${R}^{2}$$ R 2 ). It also exhibits superior predictive performance compared to models in ablation experiments, and the introduction of text data or Google trends further improves the prediction performance of the model. In conclusion, the improved AdaBoost feature-weighted combination model proposed in this study offers a more accurate prediction for semiconductor price index.

Google Trends is a popular data source that has been utilized in hundreds of studies across various fields, including information technology, business, economics, healthcare, and political science. While several previous research has addressed sampling error issues, this article focuses on the measurement errors resulting from changes in Google Trends' data collection method. By examining key phrases such as 'covid-19,' 'decoupling China,' and 'debt trap diplomacy,' this article reveals that, after 2022 when the data collection method was updated, Google Trends generates search results for terms that were not yet coined or yields too few results. These 'strange' search results indicate the presence of measurement errors in Google Trends data. Scholars are advised to validate the contents of Google Trends data through additional steps. In particular, authors in fields like business, economics, and healthcare are encouraged to replicate their research conducted before 2022 to assess the forecasting errors arising from the new data sets retrieved after that period. [ABSTRACT FROM AUTHOR]

The Australian public's concerns about a potential war with China has recently seen a sharp rise. This study utilizes Google Trends data to gauge the public agenda. Drawing from the Google Trends data in Australia between April 2013 and March 2023, several original conclusions are reached. Firstly, since 2017, the Australian public has strongly associated China-related war activities or threats of war with Australia. Secondly, since early 2020, the concerns of the Australian public towards a potential war with China has significantly increased. This result aligns with findings from conventional polls. Furthermore, these concerns reached its peak in March 2023. Lastly, the fluctuations in the Australian public's concerns regarding a potential war with China can be attributed to policy and media agendas, with their specific roles varying across different periods. This study contributes to the debate on Australia's China policy by introducing some novel empirical evidence and methods to academia. [ABSTRACT FROM AUTHOR]

Inequality among people has several unwanted effects, in humanistic, social and economic contexts. Several studies address distributional preferences among groups, but little is known about when inequality issues are focused and when and why inequality abatement measures are brought on the political agenda. We show that during the period 2004 to 2023, inequality issues were focused during elections to the EU and UK parliament and with greatest strength during the elections to the EU parliament in May 2004 and to the UK parliament in May 2015. Periods with high unemployment and inflation cause the discussion on inequality to be followed by discussions on inequality measures. However, when the discussion of inequality is followed very closely by the discussions of abatement measures, inequality aversion becomes more strongly associated with the macroeconomic variables inflation and GDP (recessions) than with unemployment and more strongly associated with the concerns for fairness than concerns with war and crime. The results were obtained examining Google Trends and scholarly studies. [ABSTRACT FROM AUTHOR]

We use Google Trends data to identify hostile sexism and find that sexism negatively predicts Clinton's vote share in the 2016 general election and is associated with lower voter turnout among those more likely to vote for a Democrat. Although we find no evidence that hostile sexism was more prevalent in states in which Trump held more than 10 pre-election rallies, we find that sexism had a larger impact on votes in these areas. This shows that the marginal effect of sexism was not uniform across the country and links the differing magnitudes of the effect to Trump rallies. [ABSTRACT FROM AUTHOR]

This study uses data from Google Trends and Twitter to analyze how public interest and sentiment towards Universal Basic Income (UBI) changed across all 50 states and Washington D.C. between 2018 and 2021. We specifically selected this time period as it includes both Andrew Yang's UBI campaign during the Democratic primaries in 2019 and the COVID-19 pandemic in 2020 when UBI gained attention due to the federal government's unconditional cash payment to almost all citizens. To overcome the limitations of sporadic opinion polls, we built on a recent development of the rescaling method to generate longitudinal Google Trends and conducted Twitter sentiment analysis. We observed a modest rise in public interest in UBI during Andrew Yang's campaign, especially in blue states, and a significant increase across all states at the onset of the COVID-19 pandemic. Although it quickly waned, the level of public attention became elevated compared to the pre-pandemic level. Contrary to previous studies, our analysis also reveals that overall sentiment became less positive after the peak interest during the pandemic, as more people engaged in online discussions. [ABSTRACT FROM AUTHOR]

Copyright of Journal der Deutschen Dermatologischen Gesellschaft is the property of Wiley-Blackwell 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: Background and objectives: In recent years, there has been an increase in skin cancer. The aim of this study was therefore to investigate the representation of skin cancer in public awareness worldwide and in Germany, and to determine whether Skin Cancer Awareness Month is represented in the search interests of the Internet‐using population in the same way as Breast Cancer Awareness Month worldwide. Data and methods: In this study, Google Trends data were used to track levels of public awareness for different tumor entities and skin cancer types worldwide and for Germany. Results: The results of this analysis clearly showed a high level of relative public search interest in breast cancer worldwide in the awareness month of October. Worldwide and in Germany, there was a certain increase in search interest and a certain seasonal effect around the May awareness month for skin cancer. For example, the analysis showed a search interest in May and during the summer months in Germany. Conclusions: It is likely that the population, for example in Germany, may benefit further from an even greater emphasis on the topic of skin cancer. [ABSTRACT FROM AUTHOR]

Purpose: This paper investigated whether a bank's popularity and depositors' fear of Google search volume could affect bank deposits and credit. Design/methodology/approach: The authors used two different quarterly data from Google Trends and banking data from 2012 Q1 to 2020 Q1. Based on available data, Google Trends data start from 2012. The authors exclude data after 2020 Q1 because the Covid-19 pandemic arguably increased the volume of Internet users due to shifting behavior to online activities. They merged and cleaned the data by winsorizing at 5 and 95 percentiles to avoid any outlier problems, reaching 74 banks in the sample. They used panel data estimation of quarterly data following Levy-Yeyati et al. (2010) and Trinugroho et al. (2020). Findings: The results show that a higher search volume of a bank's name leads to higher deposits. A higher search volume of depositor fear reduces deposits and credit. The authors also found that banks with high risk and a high search volume of their name have a significantly lower volume of deposits. Originality/value: To the best of the authors' knowledge, not many papers in banking and finance have used Google Trends data to gauge related issues regarding depositors' behavior. The authors have filled a gap in the literature by investigating whether the popularity of Google search and depositors' fear could impact deposits and credit. This study also attempted to establish whether Google Trends data could be a reliable source of information to predict depositors' behavior by using a Zscore to measure bank risk. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated the value that the Internet and web applications bring to businesses. Among other tools are those that enable the analysis and monitoring of searches, such as Google Trends, which is currently used by the fashion industry to guide experiential practices in a context of augmented reality and/or virtual reality, and even to predict purchasing behaviours through the metaverse. Data from this tool provide insight into fashion consumer search patterns. Understanding and managing this digital tool is an essential factor in rethinking businesses' marketing strategies. The aim of this study is to analyse online user search behaviour by analysing and monitoring the terms "metaverse" and "fashion" on Google Trends. A quantitative descriptive cross-sectional method was employed. The results show that there is growing consumer interest in both concepts on the Internet, despite the lack of homogeneity in the behaviour of the five Google search tools. [ABSTRACT FROM AUTHOR]

Terrorism has emerged as a paramount challenge in recent times. With current terrorist organizations adeptly embracing internet technologies and seamlessly integrating them into their strategies, the landscape of terrorism has evolved into a multifaceted global concern. For instance, the Islamic State of Iraq and Syria (ISIS) has had a negative impact not only on the citizens of the countries they directly target but also on the psychology of individuals in non-targeted countries through their internet-focused strategies. Despite not carrying out any attacks on Japan, the majority of the Japanese perceive ISIS as a significant threat to their nation. This finding serves as a prime example of how a terrorist organization like ISIS, utilizing internet strategies proficiently, can detrimentally affect individuals in non-targeted countries. This study demonstrates how the soft terrorism concept elucidates the heightened fear of ISIS in Japan. By conducting a Google Trends analysis, the Japanese case is examined in this research. The findings indicate that the soft terrorism activities of ISIS have both short-term and long-term adverse effects on the Japanese, primarily attributed to the dissemination of brutal images and videos posted by ISIS on the internet, as posited by the soft terrorism concept [ABSTRACT FROM AUTHOR]

Summary: We investigate applying and extending an exhaust data framework, using an empirical analysis to explore and compare different predictive analytic capabilities of both internal and external exhaust data for estimating sales. We use internal exhaust data that explores the relationship between app usage and web traffic data and estimation of sales and find the ability to predict sales at least 4 days ahead. We also develop predictive models of sales, using external data of Google searches, extending the previous research to include additional macroeconomic Google variables and Wikipedia pageviews, finding that we can predict at least 4 months ahead, suggesting a portfolio of exhaust data be used. We introduce the roles of internal and external exhaust data, direct and indirect exhaust data and transformed exhaust data, into an exhaust data framework. We examine what appear to be different levels of information fineness and predictability from those exhaust data sources. We also note the importance of the types of devices (e.g., mobile) and the types of commerce (e.g., mobile commerce) in creating and finding different types of exhaust. Finally, we apply an existing exhaust data framework to develop macroeconomic data exhaust variables, as the means of capturing inflation and unemployment information, using Google searches. [ABSTRACT FROM AUTHOR]

Human relationships with nature may sometimes manifest through fear, disgust and other disease‐avoidance mechanisms. While there is an evolutionary utility to these so‐called 'biophobias', many people exhibit phobic responses towards organisms that pose no tangible threats, potentially leading to excessive anxiety and avoidance of interactions with nature. Understanding the drivers of the prevalence and spread of biophobias in modern societies is, therefore, a growing concern.Here, we posit that online information‐seeking patterns may reveal general insights into biophobias. Using a culturomics approach, we gathered temporal (2004–2022) and country‐level data on the volume of internet searches for 25 biophobias, as well as 25 general phobias acting as a benchmark group. We explored temporal trends in the volume of search for each biophobias and modelled relationships between search volume for biophobias and five country‐level variables.We observed a steady increase in online search volume for biophobias between 2004 and 2022. Yet, there were marked differences in individual trends, with 17 biophobias showing positive, three negative and five stationary temporal trends. Arachnophobia (fear of spiders) attracted the most interest, followed by mysophobia (fear of microbes) and parasitophobia (fear of parasites).The United States, the United Kingdom, Australia, Canada, Mexico and India recorded wide interest in most biophobias, whereas 49% of countries showed no search volume for any biophobia. Search patterns for biophobias were strongly associated with the percentage of urban population, urban population growth and the number of extant venomous species in a given country. Conversely, search patterns for biophobias were weakly correlated with the incidence of anxiety disorders in a country's population and the likelihood of encounters with venomous animals.Our results provide quantitative support to the hypothesis that biophobias are broadly prevalent and possibly increasing as a result of widespread urbanisation and loss of experiences with nature. We suggest that people affected by biophobic disorders may be using the Internet as a key venue to seek relevant information to appraise their condition and identify coping mechanisms. These findings have broad ramifications for understanding and mitigating human–wildlife conflicts and the prevalence of widespread biophobic sentiments in modern societies. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Several sociodemographic variables are essential to the complete comprehension of people's health conditions. Also, social determinants of health are decisive in influencing people's health and healthcare strategy outcomes. Nevertheless, the level of awareness of the general population about the social determinants of health still seems poorly investigated. In this scenario, using an infodemiological approach, Google Trends represents a handy tool for monitoring internet-related search activities concerning this specific topic. This study aimed to assess the general population's consciousness about social determinants of health, testing widespread knowledge of these items and evaluating the association of the results obtained through Google-specific search volumes. MATERIALS AND METHODS: Data were collected using the Google Trends tool using several search terms related to food, social problems, and economic issues, which are useful for defining some social determinants of health variables. Descriptive data analysis was performed to show the worldwide Relative Search Volume variations from 1 September 2013 to 31 August 2023. Pearson's correlation analysis tested Relative Search Volumes and later logarithmic transformation. The K-Nearest Neighbors analysis was used to define and assess Relative Search Volumes (RSV) associations. RESULTS: The results have shown that the general population was mainly interested in topics such as "social support" and "economic burden", showing frequent peaks during the 10 years of the study. According to Pearson's coefficients test, other specific interests and relative correlations emerged regarding social variables (i.e., social support and social problems), food, and financial distress. Moreover, the K-Nearest Neighbors analysis showed that searching activities for "social support", "lack of food", and "social problems" were highly related; for "economic burden", "financial burden", and "out of pocket" suggested the existence of financial distress. CONCLUSIONS: The results of this study underline that social determinants of health are significant barriers to health and well-being and that non-medical factors should be considered more. Healthcare professionals involved in public health should study and understand more about the social determinants of health in relation to health outcomes to provide patient-centered care. Finally, this research suggests that we should encourage and maintain a more comprehensive approach to addressing the health needs of patients and communities, also by an infodemiological assessment. [ABSTRACT FROM AUTHOR]

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]