Google Search Data Research Articles

Online Searches Related to Total Laryngectomy.

To identify the most frequently asked questions regarding "laryngectomy" through an assessment of online search data. Google Search data based on the search term "laryngectomy" were analyzed using Google Trends and Search Response. The most common People Also Ask (PAA) questions were identified and classified by the concept. Each website linked to its respective PAA question was rated for understandability, ease of reading, and reading grade level. Search popularity for the term "laryngectomy" remained stable between 2017 and 2022. The most popular PAA themes were post-laryngectomy speech, laryngectomy comparison to tracheostomy, stoma and stoma care, survival/recurrence, and post-laryngectomy eating. Of the 32 websites linked to the top 50 PAA's, eleven (34%) were at or below an 8th grade reading level. Post-laryngectomy speech, eating, survival, the stoma, and the difference between laryngectomy and tracheostomy are the most common topics searched online in relation to "laryngectomy." These are important areas for both patient and healthcare provider education. N/A Laryngoscope, 133:2971-2976, 2023.

The Night Wolves: Evidence of Russian Sharp Power and Propaganda from the Victory Roads’ Itinerary

ABSTRACT The article researches the Russian Night Wolves Motorcycle Club from the perspective of the sharp power concept. It is argued that NWMC serve as a proxy for Russian sharp power, playing the role of messenger for pro-Kremlin propaganda in European countries through Victory Roads events. Using a mixed-methods Google Search data and media content analysis, the article analyzes the effectiveness of the Victory Roads events as a sharp power propaganda tool. It provides evidence that the physical presence of NWMC is indeed a powerful tool of Russian sharp power, increasing interest among the targeted population and creating long-lasting political impact.

Trends in Tattoo-Related Google Search Data in the United States: Time-Series Analysis.

Tattoos are becoming increasingly common in the United States. However, little information is available to help clinicians anticipate where, when, and on what topics patients will seek guidance regarding tattoo care, complications, and removal. The aim of this study was to model web searches concerning general interest in tattoo application, tattoo removal, and the geolocation of tattooing services. Relative search volumes (RSVs) were elicited from Google Trends, filtered to web searches made in the United States between January 15, 2008, and October 15, 2022. Longitudinal data were analyzed in GraphPad Prism and geospatial data were visualized with Datawrapper for general interest searches (tattoo and tattoo removal), aggregated geolocating searches (eg, tattoo shops near me), and symptomatic searches relating to adverse effects (eg, itchy tattoo). Results were compared to previous global literature and national surveys of tattoo prevalence. In the United States, the search terms tattoo and tattoo removal have experienced stable RSVs over the past 14 years, with both showing peaks in the summer and troughs in the winter. RSVs for search terms that geolocate tattooing services have experienced a general increase in use since 2008. A compilation of results for all collated geolocating search terms localized these searches mainly to the American South, with lesser involvement in the eastern Midwest and inland West. Increased search interest in the Southeast at the expense of more populous coastal states and Great Plains or western Midwest states reflects the ongoing harmonization of tattoo prevalence across regions, as shown by national surveys. Searches for symptoms related to adverse reactions to tattooing experienced an increase over the period of interest, with the same distribution as previous global findings. Clinicians should be aware of an increase in search interest regarding tattoos and their removal, especially during the summer months in the Southeast and Midwest. This increase in interest is occurring together with increased tattoo prevalence and increased search interest for adverse reactions in a country lagging behind in tattoo ink regulation.

Worldview defence and self‐determination theory explain the return of racial voting: Evidence from the 2016 US election

AbstractWe use self‐determination (SDT) and worldview defence theories (WDT) to explicate the psychological roots of identitarian voting in recent US, UK and EU elections. We test our theory using the 2016 US election as a case study, data from a representative sample of nearly half a million Americans, and a measure of racial animus derived from Google search data. We find that worry has a strong and significant positive association with Trump's vote share, as predicted by WDT. However, this is reversed in counties with high levels of relatedness—one of the three basic psychological needs emphasised by SDT. The positive relationship between racial animus and Trump also loses significance once an interaction between racial animus and relatedness is introduced. These results imply that identitarianism is driven at least in part by a desire for in‐group affiliation emerging out of worldview defence and unmet basic psychological needs.

Explanation of hand, foot, and mouth disease cases in Japan using Google Trends before and during the COVID-19: infodemiology study

BackgroundCoronavirus Disease 2019 (COVID-19) pandemic affects common diseases, but its impact on hand, foot, and mouth disease (HFMD) is unclear. Google Trends data is beneficial for approximate real-time statistics and because of ease in access, is expected to be used for infection explanation from an information-seeking behavior perspective. We aimed to explain HFMD cases before and during COVID-19 using Google Trends.MethodsHFMD cases were obtained from the National Institute of Infectious Diseases, and Google search data from 2009 to 2021 in Japan were downloaded from Google Trends. Pearson correlation coefficients were calculated between HFMD cases and the search topic “HFMD” from 2009 to 2021. Japanese tweets containing “HFMD” were retrieved to select search terms for further analysis. Search terms with counts larger than 1000 and belonging to ranges of infection sources, susceptible sites, susceptible populations, symptoms, treatment, preventive measures, and identified diseases were retained. Cross-correlation analyses were conducted to detect lag changes between HFMD cases and search terms before and during the COVID-19 pandemic. Multiple linear regressions with backward elimination processing were used to identify the most significant terms for HFMD explanation.ResultsHFMD cases and Google search volume peaked around July in most years, excluding 2020 and 2021. The search topic “HFMD” presented strong correlations with HFMD cases, except in 2020 when the COVID-19 outbreak occurred. In addition, the differences in lags for 73 (72.3%) search terms were negative, which might indicate increasing public awareness of HFMD infections during the COVID-19 pandemic. The results of multiple linear regression demonstrated that significant search terms contained the same meanings but expanded informative search content during the COVID-19 pandemic.ConclusionsThe significant terms for the explanation of HFMD cases before and during COVID-19 were different. Awareness of HFMD infections in Japan may have improved during the COVID-19 pandemic. Continuous monitoring is important to promote public health and prevent resurgence. The public interest reflected in information-seeking behavior can be helpful for public health surveillance.

When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage

ABSTRACT Alternative datasets are widely used for macroeconomic nowcasting together with machine learning–based tools. The latter are often applied without a complete picture of their theoretical nowcasting properties. Against this background, this article proposes a theoretically grounded nowcasting methodology that allows researchers to incorporate alternative Google Search Data (GSD) among the predictors and that combines targeted preselection, Ridge regularization, and Generalized Cross Validation. Breaking with most existing literature, which focuses on asymptotic in-sample theoretical properties, we establish the theoretical out-of-sample properties of our methodology and support them by Monte Carlo simulations. We apply our methodology to GSD to nowcast GDP growth rate of several countries during various economic periods. Our empirical findings support the idea that GSD tend to increase nowcasting accuracy, even after controlling for official variables, but that the gain differs between periods of recessions and of macroeconomic stability.

Social Media Evaluation of Seasonal and Geographic Trends of Corneal Ulcers in the United States.

The purpose of this study is to evaluate if social media and Google search data can identify seasonal and geographic trends in the incidence of corneal ulcers in the United States. This is a case series of all corneal ulcer-related data collected from two major social media platforms and Google trends from US users between 2017 and 2021. Instagram and Twitter were searched for posts and hashtags related to "corneal ulcer." Web and image search volume of "corneal ulcer" were collected from Google trends ( https://trends.google.com ). Data were compared between seasons, defined by 3-month intervals, and chi-square tests were used to determine the statistical significance. One hundred and sixty-five individuals (79% female) and 164 individuals (79% female) posted personal new corneal ulcer diagnoses on Twitter and Instagram, respectively. Summer resulted in the highest number of both Twitter (34%, P =0.07) and Instagram (33%, P =0.68) posts. Summer was also the most popular season for Google web and image searches of "corneal ulcer" (search volume average of 58.4 and 41.2, P =0.74 and P =0.01, respectively, with 100 being peak popularity). Across all platforms, the South was the most represented (32% Twitter, 38% Instagram, 32% Google Web, and 33% Google Images). Our results indicate that social media and Google trends may reflect seasonal and geographic patterns of corneal ulcer incidence in the United States. However, further study with increased power is needed.

The Impact of COVID-19 on Subjective Well-Being: Evidence from Twitter Data

This study analyzes the impact of the COVID-19 pandemic on subjective well-being as measured through Twitter for the countries of Japan and Italy. In the first nine months of 2020, the Twitter indicators dropped by 11.7% for Italy and 8.3% for Japan compared to the last two months of 2019, and even more compared to their historical means. To understand what affected the Twitter mood so strongly, the study considers a pool of potential factors including: climate and air quality data, number of COVID-19 cases and deaths, Facebook COVID-19 and flu-like symptoms global survey data, coronavirus-related Google search data, policy intervention measures, human mobility data, macro economic variables, as well as health and stress proxy variables. This study proposes a framework to analyse and assess the relative impact of these external factors on the dynamic of Twitter mood and further implements a structural model to describe the underlying concept of subjective well-being. It turns out that prolonged mobility restrictions, flu and Covid-like symptoms, economic uncertainty and low levels of quality in social interactions have a negative impact on well-being.

Leveraging Google Search Data and Artificial Intelligence Methods for Provincial-level Influenza Forecasting: A South African Case Study

This paper investigates the usefulness of Google search patterns with Artificial Intelligence (AI) techniques for timely influenza-like illness (ILI) forecasting for each of the nine South African provinces. Traditional surveillance methods are limited by delays in reporting. Existing digital disease surveillance studies that employ alternative online data have scarcely explored sub-Saharan African countries. In South Africa, Google search data has only been recently studied for ILI surveillance at the national level. Meanwhile, the differences in socio-economic and technological conditions across provinces call for a finer spatial investigation. We perform correlation analysis between Google trends (GT) data for 21 ILI-related terms and real-life ILI surveillance data for each province. Next, we develop models to assess the predictive performance of these GT data for forecasting ILI rates, using time series, machine learning, and deep learning methods. We observe sufficient correlation for only two of the nine provinces: Gauteng and Western Cape. Thus, GT data could only be used to forecast ILI in these two provinces. Interestingly, these two provinces are regarded as the most economically developed. In the other seven provinces, LSTM, a deep learning technique, gives more accurate predictions than a baseline autoregressive model when only past ILI data are used for forecasting future ILI trends. The results reveal that, for provinces for which GT data is sufficiently available, it is not only free and fast, but is an effective predictor on its own as well as when added to past ILI data for forecasting future ILI infection rates. The correlation analysis suggests an association between provincial socio-economic development and the use of digital platforms for disease surveillance. Overall, the study established the need for finer scale ILI forecasting which will inform targeted planning for disease surveillance and interventions.

The COVID-19 infodemic in Brazil: trends in Google search data.

BackgroundSince the beginning of the new coronavirus pandemic, there has been much information about the disease and the virus has been in the spotlight, shared and commented upon on the Internet. However, much of this information is infodemics and can interfere with the advancement of the disease and that way that populations act. Thus, Brazil is a country that requires attention, as despite the fact that in almost two years of pandemic it has shown a devastating numbers of deaths and number of cases, and generates false, distorted and malicious news about the pandemic. This work intends to understand the attitudes of the Brazilian population using infodemic queries from the Google Trends search tool and social and income variables.MethodsData from infodemic research carried out on Google Trends, between January 1, 2020 and June 30, 2021, with socioeconomic data, such as income and education, were unified in a single database: standardization and exploratory and multivalued techniques based on grouping were used in the study.ResultsIn the analysis of the search trend of infodemic terms, it is clear that the categories of Prevention and Beliefs should stand out in Brazil, where there is a diverse culture. It is followed by the COVID-19 Treatment category, with treatments that were not those recommended by the authorities. Income transfer programs and information on socioeconomic variables did not have much impact on infodemic surveys, but it was observed that states where President Bolsonaro has more supporters had researched more infodemic information.ConclusionsIn a country as geographically large as Brazil, it is important that political authorities go to great lengths to disseminate reliable information and monitor the infodemic in the media and on the internet. It was concluded that the denial of the pandemic and the influence of political leaders influenced the search for infodemic information, contributing to a disorganization in the control of the disease and prevention measures.

Can Google Search Data Improve the Unemployment Rate Forecasting Model? An Empirical Analysis for Turkey

Today, data accumulated during internet use have become an important source of information for people’s behaviour, issues, and needs, and due to real-time data acquisition, Google search data have become a focal point for researchers. As a result, it has been become more common to use GT data, which have been included in forecasting models for many economic indicators, including unemployment rate forecasting. Therefore, this study aims to determine whether including Google search data in forecasting models can improve the model’s performance in forecasting the unemployment rate in Turkey. In this context, out-ofsample forecasting was performed in this study using seasonally adjusted monthly unemployment rates for the period between January 2005 and August 2020 and monthly GT data about the topic of unemployment insurance. In addition, the forecasting performance of ARIMA and ARIMAX methods were compared.

Marijuana Content on Digital Media and Marijuana Use among Young People in the United States.

Health behavior theory establishes that exposure to media messages about a topic influences related knowledge, attitudes, and behavior. Marijuana-related messages proliferating on digital media likely affect attitudes and behavior about marijuana. Most research studying marijuana-related media effects on behavior relies on self-reported survey measures, which are subject to bias; people find it difficult to recall timing, frequency, and sources of messages. We calculated an exogenous measure of exposure to marijuana-related messages on digital media based on emerging public communication environment (PCE) theory. Aggregated online searches and social media posts related to marijuana for a given place reflect the marijuana-related PCE, where people are exposed to and engage with messages from multiple sources. Exogenous measures overcome bias in self-reported exposure and outcome data: simultaneity bias and endogeneity. The PCE reflects both potential exposure and relative importance of the topic in the local community, which may influence real-world marijuana use. Using 2017 Twitter and Google Search data, we measured the marijuana-related PCE to quantify where opportunities for exposure to marijuana-related posts were high and examined relationships between potential exposure and current marijuana use among youth and young adults in 2018. We found that marijuana-related online search and tweeting at the media market level are associated with offline marijuana use, controlling for demographics and state marijuana policy. The marijuana-related digital media environment may reflect and/or influence youth and young adult marijuana use. Social media and online search data offer platforms to monitor the marijuana-related PCE and supplement survey data to study media exposure and marijuana use behavior.

Relationship among Covid-19, stock price and green finance markets pragmatic evidence from volatility dynamics

This paper aims to map the effects of the rapid spread of coronavirus (COVID-19) on stock price dynamics and markets selections based on data from March 22, 2021, to September 20, 2021. Options markets from 2020 to 2021, multiple kinds of critical COVID-19 data. The proposed hypothetical modal considers investors' behavior and errors caused by the level of sentiment elicited for stock markets and green categories. This paper another element (1) Covid-19 (2) feeling, and (3) networking websites, for example, Covid-19 influence on the green size, green direction, and impact on securities prices. This paper used google search data work also creates a proxy for emotions dependent on five main categories of Data: (1) Covid-19, pandemic effect (2) markets, (3) lockdown, (4) banking and government aid. Moreover, this paper Use (a) VIX index sentiment, (b) S& The P 500 index is a measure of how well a sentiment (c) Sentiment in the S& amp;P 500 bank index. The Projected to empirical Finding follow First Level during the Covid-19, effect on jump volatility, and variability level in persistence on the green stock market exceeds that on the options market. VIX index green financial level increases with the COVID green financial level increase with the COVID-19 market index, index banking index and lockdown index. Therefore, it concluded the Share market statistic, COVID-19 benchmark, and long-run volatility. The fraction of the leap government assistance reduced. We find that the outbreak of the Pandemic of COVID-19 effects of the S&P 500 Index and S&P 500 Banks Index decrease with highest values (39%) but only after a surge in volatility covid-19 Pandemic. These results comply with our model's expectations.

Better understanding how uncertainty impacts the economy: Insights from internet search data on the importance of disaggregation

Abstract We study the impact of uncertainty shocks using a disaggregated model featuring US state-level unemployment, and uncertainty measured using state-level Google search data. Importantly, the model captures spillovers across states and identifies substantial differences in peak responses and time dynamics. Moreover, the importance of national factors in propagating the effect of uncertainty is also heterogeneous across states, but less relevant than state-level factors. These heterogeneous effects are related to state-specific industry compositions and fiscal positions. In addition, we highlight the usefulness of disaggregated data in models of uncertainty and economic activity for the USA, based on the superior ability of the disaggregate model to predict aggregate uncertainty and unemployment.

COVID-19 hospitalizations forecasts using internet search data

As the COVID-19 spread over the globe and new variants of COVID-19 keep occurring, reliable real-time forecasts of COVID-19 hospitalizations are critical for public health decisions on medical resources allocations. This paper aims to forecast future 2 weeks national and state-level COVID-19 new hospital admissions in the United States. Our method is inspired by the strong association between public search behavior and hospitalization admissions and is extended from a previously-proposed influenza tracking model, AutoRegression with GOogle search data (ARGO). Our LASSO-penalized linear regression method efficiently combines Google search information and COVID-19 related time series information with dynamic training and rolling window prediction. Compared to other publicly available models collected from COVID-19 forecast hub, our method achieves substantial error reduction in a retrospective out-of-sample evaluation from Jan 4, 2021, to Dec 27, 2021. Overall, we showed that our method is flexible, self-correcting, robust, accurate, and interpretable, making it a potentially powerful tool to assist healthcare officials and decision making for the current and future infectious disease outbreaks.

Faster indicators of chikungunya incidence using Google searches.

Chikungunya, a mosquito-borne disease, is a growing threat in Brazil, where over 640,000 cases have been reported since 2017. However, there are often long delays between diagnoses of chikungunya cases and their entry in the national monitoring system, leaving policymakers without the up-to-date case count statistics they need. In contrast, weekly data on Google searches for chikungunya is available with no delay. Here, we analyse whether Google search data can help improve rapid estimates of chikungunya case counts in Rio de Janeiro, Brazil. We build on a Bayesian approach suitable for data that is subject to long and varied delays, and find that including Google search data reduces both model error and uncertainty. These improvements are largest during epidemics, which are particularly important periods for policymakers. Including Google search data in chikungunya surveillance systems may therefore help policymakers respond to future epidemics more quickly.

Evaluating cancer awareness months and cancer screening tests through internet search big data.

e24038 Background: Cancer Awareness Months (CAMs) aim to increase public recognition of specific cancers through education and screening promotion, but their effectiveness is uncertain. This study was designed to characterize the relationship between internet search interest (ISI) of breast, colon, lung, and prostate cancers and related screening test terms with their respective CAMs with the goal of better understanding CAM effectiveness across time and geographic scale. Methods: ISI was collected from sampled Google Search data from Google Trends on a national, state, and metropolitan level between 2004 and 2021. The ISI for cancer and cancer screening terms for each respective CAM was compared to ISI for nonadjacent months and across regionality. Results: Breast CAM was associated with increased ISI for both cancer (p > 0.001) and cancer screening (p > 0.001) terms. Colon CAM was associated with higher cancer term ISI (p = 0.004), but had mixed effects on screening ISI across terms. Lung CAM was not associated with a change in cancer term ISI (p = 0.573) and had mixed effects across screening terms. Prostate CAM was not linked to ISI changes for any associated terms. Regional variation in ISI was found in all cases. Conclusions: Given the high accessibility and scalability of Google Trends, this tool can be used to practically evaluate and guide the strategies of CAM campaigns across cancer type and geographic scale. Future directions include broadening the geographic scope and comparatively assessing specific CAM campaign strategies.

Measuring the Welfare of Intermediaries

We investigate the welfare of intermediaries in oligopolistic markets where intermediaries offer additional services. We exploit the unique circumstance that in the empirical setting studied, outdoor advertising, consumers can purchase from manufacturers or intermediaries. Intermediaries provide additional services to the consumers and charge a margin for them. Intermediaries provide the following additional services: search services (information about products), purchase-aggregation services (access to quantity discounts), and consulting services. We specify an equilibrium model and structurally estimate it using market-level data. The demand includes consumers with costly search and channel-specific preferences. The supply includes two distribution channels. One features bargaining about wholesale prices between manufacturers and intermediaries and downstream price competition. The other is vertically integrated. We show how Google search data can be used to identify the search-cost parameters. We use the estimated model to simulate counterfactual scenarios where intermediaries do not offer additional services. We find that the three services considered provide value to consumers, with search playing a prominent role. Our analysis helps explain why intermediaries are ubiquitous in modern economies despite the double marginalization. This paper was accepted by Matthew Shum, marketing. Funding: The authors acknowledge the allocation of computing time from the Ohio Supercomputer Center [Grant PAS1350-2]. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001. This work was also supported by Fundação para a Ciência e a Tecnologia [Grant UIDB/00315/2020]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2021.4266 .

Relative timing of respiratory syncytial virus epidemics in summer 2021 across the United States was similar to a typical winter season.

We used a validated proxy of respiratory syncytial virus (RSV) activity in the United States (Google search data) to evaluate the onsets of RSV epidemics in 2021 and 2016–2019. Despite the unusual out‐of‐season summer timing, the relative timing of RSV epidemics between states in 2021 shared a similar spatial pattern with typical winter RSV seasons. Our results suggest that the onset of RSV epidemics in Florida can serve as a baseline to adjust the initiation of prophylaxis administration and clinical trials in other states regardless of the seasonality of RSV epidemics.

The national burden of scabies in Germany: a population-based approach using Internet search engine data

PurposeScabies is a World Health Organization-defined neglected tropical disease and a growing public health issue worldwide. It is difficult to obtain reliable data on prevalence due to the lack of standardized tests. The aim of this study was to assess scabies online search behavior in Germany to identify local differences using Google search volume.MethodsGoogle Ads Keyword Planner was used to investigate the scabies-related search volume for Germany as a whole, its 16 federal states, and 15 large cities for the period from January 2016 to December 2019. The identified search terms were qualitatively categorized and critically analyzed.ResultsA total of 572 keywords with an overall search volume of 11,414,180 searches regarding scabies were identified in Germany. The number of searches was higher in winter than in summer, with a national peak in March 2018. Around 30.6% of the searches regarding scabies therapy (n = 978,420) were related to home remedies. Regarding body localization, most searches focused on the whole body (n = 109,050), followed by head (n = 89,360) and the genital area (n = 28,640).ConclusionsThe analysis of Google search data provides an overview of the populations’ interest regarding scabies. The analysis can detect local peaks and assess the relevance of scabies at individual localizations of the body. The study highlighted current possible shortcomings in the therapy of scabies. It also underlined the importance of improving awareness regarding scabies so that affected individuals can consult a doctor earlier for treatment.