Google Trends Search Research Articles

Evaluation of Public Interest in Mohs Surgery and Other Elective Surgical Procedures During the COVID-19 Pandemic.

The reallocation of health care resources to focus on the acute care needs of COVID-19 patients leads to a delay and deferral of outpatient surgical procedures such as Mohs surgery. Planning for the resumption of regular outpatient surgical care and preparing for future surges in COVID-19 cases requires identifying surrogate markers of health care demand. United States national and state-based Google search data for "Mohs surgery" and other common elective surgical and cosmetic procedures were evaluated. These were compared with national and state-wide COVID-19 case number and death data from the Johns Hopkins University. Pearson correlation coefficients were generated to assess the association between COVID-19 cases and deaths with Google search trends. Search volume for "Mohs surgery" and other elective surgical and cosmetic procedures significantly decreased as the number of new deaths from COVID-19 increased. Statistically significant inverse correlation was noted between "Mohs surgery" search volume and new COVID-19 deaths on a national and state-based level. Search metric analysis may be used as part of a big data model to help predict health care demand during the reopening phase of the COVID-19 pandemic.

Associations between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States.

We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19-related symptoms. Specifically, we represent state-level time series data for COVID-19 and Google search trends for symptoms as smoothed functional curves. Given these functional data, we explore the modes of variation in the data using functional principal component analysis (FPCA). We also apply functional clustering analysis to identify patterns of COVID-19 confirmed case and death trajectories across the US. Moreover, we quantify the associations between Google COVID-19 search trends for symptoms and COVID-19 confirmed case and death trajectories using dynamic correlation. Finally, we examine the dynamics of correlations for the top nine Google search trends of symptoms commonly associated with COVID-19 confirmed case and death trajectories. Our results reveal and characterize distinct patterns for COVID-19 spread and mortality across the US. The dynamics of these correlations suggest the feasibility of using Google queries to forecast COVID-19 cases and mortality for up to three weeks in advance. Our results and analysis framework set the stage for the development of predictive models for forecasting COVID-19 confirmed cases and deaths using historical data and Google search trends for nine symptoms associated with both outcomes.

Correction: Association Between State Indoor Tanning Legislation and Google Search Trends Data in the United States From 2006 to 2019: Time-Series Analysis.

[This corrects the article DOI: 10.2196/26707.].

Association Between State Indoor Tanning Legislation and Google Search Trends Data in the United States From 2006 to 2019: Time-Series Analysis.

Exposure to ultraviolet radiation from the sun or indoor tanning is the cause of most skin cancers. Although indoor tanning has decreased in recent years, it remains most common among adolescents and young adults, whose skin is particularly vulnerable to long-term damage. US states have adopted several types of legislation to attempt to minimize indoor tanning among minors: a ban on indoor tanning among all minors, a partial minor ban by age (eg, <14 years), or the requirement of parental consent or accompaniment for tanning. Currently, only 6 US states have no indoor tanning legislation for minors. This study investigated whether internet searches (as an indicator of interest) related to indoor tanning varied across US states by the type of indoor tanning legislation, using data from Google Trends from 2006 to 2019. We conducted a time-series analysis of Google Trends data on indoor tanning from 2006 to 2019 by US state. Time-series linear regression models were generated to assess the Google Trends data over time by the type of indoor tanning legislation. We found that indoor tanning search rates decreased significantly for all 50 states and the District of Columbia over time (P<.01). The searches peaked in 2012 when indoor tanning received marked attention (eg, indoor tanning was banned for all minors by the first state-California). The reduction in search rates was more marked for states with a complete ban among minors compared to those with less restrictive types of legislation. Our findings are consistent with those of other studies on the association between indoor tanning regulations and attitudinal and behavioral trends related to indoor tanning. The main limitation of the study is that raw search data were not available for more precise analysis. With changes in interest and norms, indoor tanning and skin cancer risk among young people may change. Future studies should continue to determine the impact of such public health policies in order to inform policy efforts and minimize risks to public health.

The Causality Inference of Public Interest in Restaurants and Bars on Daily COVID-19 Cases in the United States: Google Trends Analysis.

BackgroundThe COVID-19 pandemic has affected virtually every region in the world. At the time of this study, the number of daily new cases in the United States was greater than that in any other country, and the trend was increasing in most states. Google Trends provides data regarding public interest in various topics during different periods. Analyzing these trends using data mining methods may provide useful insights and observations regarding the COVID-19 outbreak.ObjectiveThe objective of this study is to consider the predictive ability of different search terms not directly related to COVID-19 with regard to the increase of daily cases in the United States. In particular, we are concerned with searches related to dine-in restaurants and bars. Data were obtained from the Google Trends application programming interface and the COVID-19 Tracking Project.MethodsTo test the causation of one time series on another, we used the Granger causality test. We considered the causation of two different search query trends related to dine-in restaurants and bars on daily positive cases in the US states and territories with the 10 highest and 10 lowest numbers of daily new cases of COVID-19. In addition, we used Pearson correlations to measure the linear relationships between different trends.ResultsOur results showed that for states and territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly occurred after reopening, significantly affected the number of daily new cases on average. California, for example, showed the most searches for restaurants on June 7, 2020; this affected the number of new cases within two weeks after the peak, with a P value of .004 for the Granger causality test.ConclusionsAlthough a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases in US states and territories with higher numbers of daily new cases. We showed that these influential search trends can be used to provide additional information for prediction tasks regarding new cases in each region. These predictions can help health care leaders manage and control the impact of the COVID-19 outbreak on society and prepare for its outcomes.

Conjecturing Fearful Futures: Journalistic Discourses on Deepfakes

ABSTRACT This paper investigates journalistic discourses on “deepfakes” as the future of fake news. We analyze journalistic discourses on deepfakes over an 18-month period from 1 January 2018, when a GoogleTrends search demonstrates that the term first began to circulate, to 1 July 2019, shortly after an altered video of Nancy Pelosi, the US Speaker of the House, circulated on social media. Based on a comprehensive thematic analysis of English-language news stories on the topic, drawn from Nexis UK, we suggest that journalistic responses to deepfakes reveal deeper anxieties both about the future of the information environment and journalism’s role within this environment. Concerns that the audiovisual nature of deepfakes makes them inherently more believable than previous fake news forms leads to worries over the impending weaponization of deepfakes by resource-rich “bad actors.” At stake is the trustworthiness of media content, and journalists’ role in providing verified content to the public. We argue that journalists conjure up speculative worst-case scenarios around deepfakes—what we refer to as “conjectured specifity” to highlight the vital importance of journalism as a bulwark against fabrication and a defender of truth.

The COVID-19 storm and the energy sector: The impact and role of uncertainty

Prior research has shown that energy sector stock prices are impacted by uncertainty. The coronavirus (COVID-19) pandemic has given rise to widespread health and economic-related uncertainty. In this study, we investigate the magnitude and the timing of the impact of COVID-19 related uncertainty on returns and volatility for 20 national energy indices and a global energy index using ARCH/GARCH models. We propose a novel “overall impact of uncertainty” (OIU) measure, explained using a natural phenomenon analogy of the overall impact of a rainstorm, to gauge the magnitude and intensity of the impact of uncertainty on energy sector returns. Drawing upon economic psychology, COVID-19 related uncertainty is measured in terms of searches for information relating to COVID-19 as captured by Google search trends. Our results show that the energy sectors of countries further west from the outbreak of the virus in China are impacted to a greater extent by COVID-19 related uncertainty. A similar observation is made for net energy and oil exporters relative to importers. We also find that the impact of uncertainty on most national energy sectors intensified and then weakened as the pandemic evolved. Additional analysis confirms that COVID-19 uncertainty is part of the composite set of factors that drive energy sector returns over the COVID-19 period although its importance has declined over time.

Pro-Ana and Pro-Mia keywords, their google search trend

IntroductionEating Disorders (ED) have increased both in number of cases and diagnoses in recent years, partly due to the ease of searching on the Internet. This “community” as they call themselves has a proper language, which makes them easier to connect.ObjectivesTo know the search frequency of Pro-Ana and Pro-Mia terms in Spanish in the Google search engine.MethodsA manual screening was carried out based on the word analysis of Pro-Ana and Pro-Mia blogs to obtain their search frequency in Spanish. Using the Google Trends tool, a total of 19 word combinations related to ED and their advocacy were reviewed in the time period from 01/01/2019 to 01/12/20. Some of them such as: “carrera de kilos” (kilos race), “princesa de cristal” (glass princess), “princesa de porcelana” (porcelain princess) and “dieta ABC” (ABC diet) among other terms.ResultsFrom 2019 to 2020 there has been an increase in the searches related to Eating Disorders (41.63%), ABC diet (9.72%), porcelain princess (25.52%) and kilos race (38.53%). There has also been a decrease in the search for thinspo ana (30.9%), tips ana (4.15%), blog mia (13.09%) or blog ana (0.79%).ConclusionsSearch trends change over time as they meet the evolving needs. In several media we can find a clear increase in ED during this 2020 due to the confinement related to COVID-19. This is something that we can also relate to this increase in searches for some terms.DisclosureNo significant relationships.

The Relationship Between Cancer Incidence and Awareness Activities and Google Online Search Volume in Turkey: A Retrospective Research

Objective: The aim of this study was to investigate the relationship between cancer incidence and Google search volumes for the six common types of cancer in Turkey. A second aim of the research is to determine whether online search activities are affected by awareness months for different cancer types. Study Design: A retrospective study was conducted by comparing the incidence of cancer between 2006 and 2017 for the cancers of lung, breast, stomach, prostate, colon and bladder published by IHME (The Institute for Health Metrics and Evaluation) and the Google Trends Search Volume designed by Google. The relationship between annual cancer incidence and Google Search Volume for selected cancer types was tested by Spearman Correlation analysis. Then, it was tested whether there was a difference in Google Search Volume averages of cancer types by months. Results: Google Search Volume has been found to have a significant and positive relationship with the incidence of stomach, colon and bladder cancers. In addition, it was concluded that cancer online searches were affected by awareness campaigns. Breast cancer had the highest Google search volume in October, which was determined as “Breast Cancer Awareness Month” and colon cancer was high in March, which was determined as “Colon Cancer Awareness Month”. Conclusion: These results indicate that online searches may reflect public awareness of the underlying cancer epidemiology. Enhanced understanding of online search patterns can increase traditional epidemiological surveillance, provide opportunities for targeted patient engagement, and enable the assessment of community awareness activities.

Public precaution awareness: a case study from Google search trend during Covid-19 outbreak in Indonesia

Objective: The global outbreak of COVID-19 is emerged recently and this poses stress on health and well-being of the people in the worldwide including in Indonesia. Public interest in this new issue largely reflects people’s attention to COVID-19 and their willingness to take precautionary actions. This study aimed to examine public awareness on the prevention of COVID-19 using Google Trend (GT) . Methods: Using GT, we retrieved public query data for the term of (coronavirus, handwashing, hand sanitizer and facemask) between 12 December 2019 and 4 April 2020 in the specific locations of Indonesia nationwide and several provinces. The correlation between daily search volumes on the topic related to COVID-19 and the daily number of people infected with COVID-19 was analyzed. Results: Our studies observed the overall search trend of RSV regarding COVID-19 after the global outbreak and the announcement of first covid-19 case in Indonesia, the search related to COVID-19, and other defined terms in Indonesia increased rapidly. In addition, search related to face mask were gradually increased. Searches for face mask and hand sanitizer is more popular than handwashing. There is a positive correlation between daily RSV on COVID-19 and other defined terms and the daily number of people infected with COVID-19 nationwide ( p <0·05). There are various interest to RSV related to COVID-19, and public awareness of COVID-19 was almost similar in some provinces, particularly searches related to facemask and hand sanitizer. Conclusion: Our findings demonstrates that GT could potentially define the proper timing and location for practicing appropriate risk communication strategies to prevent the spread of emerging disease. The study demonstrates that government should strengthen the publicity of COVID- 19 nationally, reinforce the public's awareness on the prevention of COVID-19 and finally inform the importance of protecting individuals with appropriate and affordable precautionary measures.

Ocular-symptoms-related Google Search Trends during the COVID-19 Pandemic in Europe

PurposeTo investigate the correlation between the daily relative search volume (RSV) of keywords related to coronavirus and ocular symptoms, and the numbers of the daily coronavirus disease 2019 (COVID-19) new cases and new deaths in Europe using Google Trends (GT).MethodsThe RSVs of the selected keywords related to coronavirus and ocular symptoms between 24 January and 1 June 2020 in the United Kingdom, Spain, Italy, Germany and Turkey were accessed from GT. The numbers of daily COVID-19 new cases and new deaths in the same time interval in the aforementioned countries were compared with the RSVs of the keywords and terms.ResultsThe United Kingdom, Spain, Italy, Germany, and Turkey were the most affected countries from the COVID-19 pandemic in Europe. In these countries, statistically significant and strong correlations were found between the daily RSVs of most keywords related to coronavirus and ocular symptoms, and the daily numbers of COVID-19 new cases and new deaths ranging from 0.175 to 0.807.ConclusionsOcular symptoms are not the frequent signs of COVID-19, but the ocular surface is a possible route for the transmission of COVID-19. The preliminary outcomes of this study demonstrated that there are significant correlations with the ocular symptoms and COVID-19. The news about the relation of eye and COVID-19 in the mass media may have influenced the Google internet search activity of the public for ocular symptoms.

Predicting Retail Mobility during the COVID-19 Pandemic

Much has changed in the wake of the COVID-19 pandemic, consumer behavior notwithstanding. In this study, we explore the drivers of retail mobility in several metropolitan areas in the United States under the perceived risks of COVID-19. We measure risk using both local and national case and death counts of COVID-19 and assess the response to these risks using real-time Google Trends searches for personal protective equipment (PPE) including masks, hand sanitizer, and disinfectant. We further investigate the effects of perceived risk, Google Trends searches for PPE, and COVID-19 tweets on retail mobility, as measured via real-time Google Mobility data. We focus our analysis on the period immediately preceding and following the formal declaration of a pandemic when the first nation-wide social distancing policies were implemented, and uncertainty regarding the severity and transmissibility of the virus was the highest. To conduct our investigation, we develop a novel optimization scheme and procedure to induce linear models containing strictly significant covariates and no multicollinearity, which we then analyze. Our findings suggest that there are a variety of unique factors that contribute to and drive retail mobility in different geographic locations, as well as several factors that are common to all locations.

Using Google Trends and Twitter for Prostate Cancer Awareness: A Comparative Analysis of Prostate Cancer Awareness Month and Breast Cancer Awareness Month.

BackgroundWe evaluated (1) whether the public interest in prostate cancer and prostate cancer screening increased following Prostate Cancer Awareness Month (PCAM) and (2) whether PCAM was as effective as Breast Cancer Awareness Month (BCAM) at generating public interest.Methods Using Google Trends, we measured search volume in PCAM and BCAM. We used the search volume in Google Trends as a proxy for changes in public interest from January 01, 2009 to December 31, 2018 worldwide, including the specific keywords: “Prostate Cancer”; “Prostate-Specific Antigen”; “Prostate Cancer Screening”; “Prostate Cancer Management”; “Breast Cancer”; “Breast Cancer Screening”; “Mammography”; and “Breast Cancer Management”. Also, we measured tweets containing “prostate cancer” and “breast cancer”. We used an autoregressive integrated moving algorithm (ARIMA) to forecast expected weekly search volumes during PCAM and BCAM. We then compared the Google Trends data from during PCAM and BCAM to the forecasted values and determined a “greater than expected” range.ResultsThe mean pooled percent increase in tweets associated with “prostate cancer” during PCAM from 2012 through 2018 was 15.9% (95% CI, -1% - 33%). The mean pooled percent increase in tweets associated with “breast cancer” during BCAM from 2012 through 2018 was 318.5% (95% CI, 268% - 369%). BCAM was associated with a 302.6% greater effect on increasing tweets referencing the disease of interest than PCAM from 2012-2018. “Breast cancer” Google searches were found to be 36.7% (95% CI, 34% - 39%) more frequent than “prostate cancer” per month from 2009-2019. Google Searches for “breast cancer screening” were 29.6% (95% CI, 28% - 31%) greater than “prostate cancer screening”.ConclusionsOur results indicate that PCAM is not generating substantial internet interest, especially when compared to BCAM. The search volume for Google Trends search terms related to PCAM was less than BCAM in every comparison, and Twitter indicated only a slight increase of Tweets during the month of PCAM. Suggestions are provided to improve the effect of PCAM and men’s health.

The only certainty is uncertainty: An analysis of the impact of COVID-19 uncertainty on regional stock markets

Uncertainty surrounding COVID-19 is widespread. We investigate the timing and quantify the impact of COVID-19 related uncertainty on returns and volatility for regional market aggregates using ARCH/GARCH models. Drawing upon economic psychology, COVID-19 related uncertainty is measured by searches for information as reflected by Google search trends. Asian markets are more resilient than others. Latin American markets are most impacted in terms of returns and volatility. For most regions, there is evidence of an increasing impact of COVID-19 related uncertainty which dissipates as the crisis evolves. We confirm that Google search trends capture uncertainty by comparing this measure against alternative uncertainty measures.

Googling for suicide in Croatia: A mixed-methods study

Google search trends have shown promise for predicting suicide deaths. We examined the relationship between search trends data for suicide-related search terms and monthly suicide deaths (2014–2018) in Croatia. We identified two suicide prevention search terms, samoubojstvo and suicid (engl. suicide), where an increase in searches preceded a decrease in suicides, and one suicide risk term, kako se ubiti (engl. how to kill yourself), where an increase in searches preceded an increase in suicides. On webpages elicited by suicide-related search terms, factual information about suicide was most common. Results imply the need for a comprehensive online suicide prevention strategy.

Boosting Cryptocurrency Return Prediction

We use boosted decision trees to generate daily out-of-sample forecasts of excess returns for Bitcoin and Ethereum, the two best-known and largest cryptocurrencies. The decision trees incorporate information from 39 predictors, including variables relating to cryptocurrency fundamentals, technical indicators, Google Trends searches, Reddit comments, and articles from Factiva. We use the XGBoost algorithm to boost trees and find that excess return forecasts based on boosted trees produce statistically and economically significant out-of-sample gains. We explore the importance of individual predictors and nonlinearities in the fitted boosted trees. We find that a broad array of predictors are relevant for forecasting daily cryptocurrency returns and that strong nonlinearities characterize the predictive relationships.

Advanced Early Dengue Prediction and Exploration Service (AEDES)

This paper presents Project AEDES, a big data early warning, and surveillance system for dengue. The project utilizes Google Search Trends to detect public interest and panics related to dengue. Using Google Search Trends, precipitation, and temperature readings from climate data, the system nowcasts probable dengue cases and dengue-related deaths. The system utilizes FAPAR, NDVI, and NDWI readings from remote sensing to detect likely mosquito hotspots to prioritize interventions. We discuss the origin and development of the project and recent developments. We also discuss the current state of development and directions for further work.

The COVID-19 Storm and the Energy Sector: The Impact and Role of Uncertainty

Prior research has shown that energy sector stock prices are impacted by uncertainty. The coronavirus (COVID-19) pandemic has given rise to widespread health and economic-related uncertainty. In this study, we investigate the impact and the timing of the impact of COVID-19 related uncertainty on returns and volatility for 20 national energy indices and a global energy index using ARCH/GARCH models. We propose a novel ‘overall impact of uncertainty’ (OIU) measure, explained using a natural phenomenon analogy of the overall impact of a rainstorm, to gauge the magnitude and intensity of the impact of uncertainty on energy sector returns. Drawing from economic psychology, COVID-19 related uncertainty is measured in terms of searches for information relating to COVID-19 as captured by Google search trends. Our results show that the energy sectors of countries further west from the outbreak of the virus in China are impacted to a greater extent by COVID-19 related uncertainty. A similar observation is made for net energy and oil exporters relative to importers. We also find that the impact of uncertainty on most national energy sectors intensified and then weakened as the pandemic evolved. Additional analysis confirms that COVID-19 uncertainty is part of the composite set of factors that drive energy sector returns over the COVID-19 period although its importance has declined over time.

Community Detection in Google Searches Related to “Coronavirus”

Coronavirus and the COVID-19 pandemic have substantially altered the ways in which people learn, interact, and discover information. In the absence of everyday in-person interaction, how do people self-educate while living in isolation during such times? More specifically, do communities emerge in Google search trends related to coronavirus? Using a suite of network and community detection algorithms, we scrape and mine all Google search trends in America related to an initial search for “coronavirus,” starting with the first Google search on the term (January 16, 2020) to recently (August 11, 2020). Results indicate a near-constant shift in the structure of how people educate themselves on coronavirus. Queries in the earliest days focusing on “Wuhan” and “China”, then shift to “stimulus checks” at the height of the virus in the U.S., and finally shift to queries related to local surges of new cases in later days. A few communities emerge surrounding terms more overtly related to coronavirus (e.g., “cases”, “symptoms”, etc.). Yet, given the shift in related Google queries and the broader information environment, clear community structure for the full search space does not emerge.

Increasing utility of Google Trends in monitoring cardiovascular disease.

IntroductionCardiovascular disease is the most common cause of morbidity and mortality in the United States. Patients are increasingly using internet search to find health-related information, including searches for cardiovascular diseases and risk factors. We sought to evaluate the change in the state by state correlation of cardiovascular disease and risk factors with Google Trends search volumes.MethodsData on cardiovascular disease hospitalizations and risk factor prevalence were obtained from the publically available Centers for Disease Control and Prevention website from 2006 to 2018. Google Trends data were obtained for matching conditions and time periods. Simple linear regression was performed to evaluate for an increase in correlation over time.ResultsHospitalizations for six separate cardiovascular disease conditions showed moderate to strong correlation with online search data in the last period studied (heart failure (0.58, p < .001), atrial fibrillation (0.57, p < .001), coronary heart disease (0.58, p < .001), myocardial infarction (0.70, p < .001), stroke (0.62, p < .001), cardiac dysrhythmia (0.46, p < .001)) in the United States. All diseases studied showed a positive increase in correlation throughout the time period studied (p < .05). All five of the cardiovascular risk factors studied showed strong correlation with online search data; diabetes (R = 0.78, p < .001), cigarette use (R = 0.79, p < .001), hypertension (R = 0.81, p < .001), high cholesterol (R = 0.59, p < .001), and obesity (p = 0.80, p < .001) in the United States. Three of the five risk factors showed an increasing correlation over time.ConclusionThe prevalence of and hospitalizations for cardiovascular conditions in the United States strongly correlate with online search volumes in the United States when analyzed by state. This relationship has progressively strengthened or been strong and stable over recent years for these conditions. Google Trends represents an increasingly valuable tool for evaluating the burden of cardiovascular disease and risk factors in the United States.