Search Query Volume Research Articles

Heteroskedasticity in excess Bitcoin return data: Google trend vs. GARCH effects

This paper examines the mixture of distribution properties associated with heteroskedastic excess Bitcoin return data, using the volume of Google search queries as a proxy for the information arrival time, from a monthly data sampling period of June 2010 to May 2019. The results show that the volatility coefficients become highly statistically insignificant when the lagged volume of search queries is included in the conditional variance equation of the GJR-GARCH-M model. This clearly suggests that the volume of search queries is shown to provide significant explanatory power regarding the variance of heteroskedastic excess Bitcoin return, which can be traced from the ARCH process defined in the GJR-GARCH-M specification. A significant negative relationship between the conditional volatility and the volume of search queries indicates that Internet (online) information arrival reduces the risk premium in the Bitcoin market, which may improve market stability.

Seasonal and Geographic Patterns in Seeking Cardiovascular Health Information: An Analysis of the Online Search Trends

ObjectiveTo ascertain whether temporal and geographic interest in seeking cardiovascular disease (CVD) information online follows seasonal and geographic patterns similar to those observed in real-world data. MethodsWe searched Google Trends for popular search terms relating to CVD. Relative search volumes (RSVs) were obtained for the period January 4, 2004, to April 19, 2014, for the United States and Australia. We compared average RSVs by month and season and used cosinor analysis to test for seasonal variation in RSVs. We also assessed correlations between state-level RSVs and CVD burden using an ecological correlational design. ResultsRSVs were 15% higher in the United States and 45% higher in Australia for winter compared with summer (P<.001 for difference for both). In the United States, RSVs were 36% higher in February compared with August, while in Australia, RSVs were 75% higher in August compared with January. On cosinor analysis, we found a significant seasonal variability in RSVs, with winter peaks and summer troughs for both the United States and Australia (P<.001 for zero amplitude test for both). We found a significant correlation between state-level RSVs and mortality from CVD (r=0.62; P<.001), heart disease (r=0.58; P<.001), coronary heart disease (r=0.48; P<.001), heart failure (r=0.51; P<.001), and stroke (r=0.60; P<.001). ConclusionGoogle search query volumes related to CVD follow strong seasonal patterns with winter peaks and summer troughs. There is moderate to strong positive correlation between state-level search query volumes and burden of CVD mortality.

Does investor attention to energy stocks exhibit power law?

In this article, we explore the dynamics of memory and the correlation structure in investor attention. Information on whether a time series is a pure noise or has a short or long range memory is important for predictive modeling of the series and analyzing its relationship with other variables. Existing studies on time series memory of stock variables are limited to price, volume, returns and conditional variance of returns. We carry out an in-depth analysis to understand the dependence structure of stock's investor attention time series (IATS) for energy market. In this process, we first investigate the existence of correlated dependence in IATS and analyze its dynamics using detrended fluctuation analysis. Further, we check how this dependence changes a) with frequency of sampled data b) during volatility clustering periods of price and volume of underlying stocks. Finally, we perfom detrended cross correlation analysis on local and global IATS to find any cross correlation dependence between the two. We use relative search query volume in Google to quantify IATS for a particular stock and analyze top 20 energy companies based on their market capitalization. The obtained detrended fluctuation coefficients for stock's IATS differ significantly from 0.5 indicating the existence of memory in the series. The dynamics of this memory exhibits persistent and mean reverting behavior. We also observed a reasonably high positive cross correlation dependence between local and global IATS. Finally we find that volatility clustering has little effect on dependence structure of investor attention time series. We also include a few examples to illustrate practical implications of these results.

Using the Baidu Search Index to Predict the Incidence of HIV/AIDS in China

Based on a panel of 30 provinces and a timeframe from January 2009 to December 2013, we estimate the association between monthly human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS) incidence and the relevant Internet search query volumes in Baidu, the most widely used search engine among the Chinese. The pooled mean group (PMG) model show that the Baidu search index (BSI) positively predicts the increase in HIV/AIDS incidence, with a 1% increase in BSI associated with a 2.1% increase in HIV/AIDS incidence on average. This study proposes a promising method to estimate and forecast the incidence of HIV/AIDS, a type of infectious disease that is culturally sensitive and highly unevenly distributed in China; the method can be taken as a complement to a traditional HIV/AIDS surveillance system.

An infodemiological study using search engine query data to explore the temporal variations of depression in Finland

A majority of healthcare is undertaken by individuals without the involvement or knowledge of healthcare professionals. People often try to treat health problems themselves, often first consulting the Internet, with search engines as natural starting points. Health information seeking conducted in search engines generate big data, data that can provide valuable insights into patterns of symptoms and disease, especially for stigmatizing or sensitive health topics, like mental health problems. The aim of this article is to utilize search engine query data for trends and temporal variations of depression in Finland. The key findings of this study show that depression related Internet search query volumes are slightly increasing during the time periods studied. The results also show that search query volumes follow seasonal patterns with peaks during autumn and spring and troughs during the summer months and mid-winter. Of all weekdays, Sundays have the highest search volume for depression related queries. These results present new meaningful insights into the epidemiology of depression, as it can give insights into the part of the population that does not present for treatment or professional help. It can also help health professionals and other officials to understand broader patterns of mental illness when planning services and campaigns.

Google Trends and reality: Do the proportions match?: Appraising the informational value of online search behavior: Evidence from Swiss tourism regions

This study examines the extent to which real-world economic activity is reflected in aggregate online search behavior on Google Search. As opposed to previous studies, being subject to potential mismeasurement problems when examining search queries along their longitudinal dimension, we apply an alternative investigative approach that exploits the cross-sectional instead of the longitudinal informational content embodied in Googles data. Moreover, while previous studies most often examine a single Google Trends series, our analyses are based on over 60 distinct series, which allow us to assess how informative the data are, not only within each series, but also between series. Finally, our Google Trends indices are based on the recently launched Google Knowledge Graph technology, allowing for a remarkably accurate measurement of relevant search query volumes. We assess the informational value of the data as strong, semi-strong, or weak based on unbiasedness and efficiency considerations in a Mincer–Zarnowitz-type regression model. Here, the context of (Swiss) tourism demand proves particularly useful, and we find that search-based tourism demand predictions are, on average, highly accurate approximations of reality. This indicates that search-based indicators may serve as valuable real-time complements for the guidance of economic policy.

Fines versus prison for the issuance of bad checks: Evidence from a policy shift in Turkey

We investigate whether the February 2012 amendments to the Check Law in Turkey that replaced imprisonment with administrative fines for issuing bad checks were a driver of the surge in bad checks since late 2011. As the change in the law was foreseen, we argue that check issuance behavior was altered before the new law became official. To capture this, we use the cumulative volume of internet search queries related to the upcoming legal change. We find that, unlike the case during the global financial crisis of 2008–09, the surge in bad checks occurring in 2011–12 cannot be accounted for by the state of the economic environment unless the effects of the February 2012 law change are also controlled for. We also provide evidence that economic agents adjust fairly rapidly to the legal change, which reverses the surge in bad checks within a year. Overall, our findings suggest that sanctions need not be harsh to deter non-violent offenses provided that appropriate institutional structures are in place.

Google Trends and cycles of public interest in biodiversity: the animal spirits effect

The paper introduces the idea of using Google Trends search query volumes for both economic and biodiversity-related terms and keywords as data source in order to produce a composite but simple indicator-early warning sign of public interest in conservation co-evolving with citizens’ concerns about economy and unemployment. The behavior of this indicator is examined mainly in the context of the European Union Member States facing the effects of economic recession after the 2008 economic crisis. Four EuroArea Member States (Germany, Greece, Italy and The Netherlands) representing various facets of the combination of biodiversity and economy conditions are used as examples of the characteristics of this indicator, during a ten year period, extending equally before and after the eruption of economic crisis. Results indicate that such an indicator uncovers that public interest in biodiversity does decelerate worldwide, in the European Union and in the four studied cases; however, an explanation of this decreasing trend as being part of larger cycles in public interest that resonate with some hysteresis with cycles of economy offers a more promising view of the phenomenon. The hypothesis that public interest in biodiversity is driven by “animal spirits”, according to Keynes’ prediction is formulated and its significance regarding communication and strategy of conservation is debated.

P.049 Seasonal variations in aneurysmal subarachnoid hemorrhage: revisiting the myth using google trends

Background: Results of previous studies examining seasonal variation in the incidence of aneurysmal subarachnoid hemorrhage (SAH) are conflicting. The aim of this study is to investigate whether there is a seasonal effect in online search queries for SAH that may reflect an association between meteorological factors and aneurysm rupture. Methods: We utilized the Google Trends data service to analyze the volume of internet queries for SAH on Google’s search engine from January 1, 2004 to November 2016. We used comprehensive search terms and collected data from: USA, Canada, Finland, and Japan, as well as worldwide search volume. Potential seasonal variations in the data were assessed by comparative non-parametric tests and curve-fit regression model. Results: Our analyses revealed that USA had the highest median search scores (115 vs. 86, 46, 46 for Finland, Canada and Japan, respectively). The term “brain aneurysm” was the commonly used search term among countries, followed by “cerebral aneurysm”. There was no evidence of seasonality in any of the countries studied on both univariate tests and regression time-adjusted analysis. Conclusions: There are no seasonal variations in internet search query volume for SAH. Further studies are needed to explore whether online search volumes correlate with the actual incidence of SAH.

Internet search volumes in brain aneurysms and subarachnoid hemorrhage: Is there evidence of seasonality?

ObjectiveResults of previous studies examining seasonal variation in the incidence of aneurysmal subarachnoid hemorrhage (SAH) are conflicting. The aim of this brief report is to investigate whether there is a seasonal effect in online search queries for SAH that may reflect an association between meteorological factors and aneurysm rupture. Patients and methodsWe used the Google Trends data service to analyze the volume of internet queries for SAH on Google’s search engine from January 1, 2004 to November 2016. We used comprehensive search terms and collected data from: USA, Canada, and countries known for their high prevalence of SAH (Finland, and Japan), as well as worldwide search volume. Potential seasonal variations in the data were assessed by comparative non-parametric tests and curve-fit regression model. ResultsOur analyses revealed that USA had the highest median value in cumulative search scores (115 vs. 86, 46, 46 for Finland, Canada and Japan, respectively). The term “brain aneurysm” was the commonly used search term among countries, followed by “cerebral aneurysm”. There was no evidence of seasonality in any of the countries studied on both univariate tests and regression time-adjusted analysis. ConclusionsThere are no seasonal variations in internet search query volume for SAH. Further studies are needed to explore whether online search volumes correlate with the actual incidence of SAH.

Web search query data and prescription volumes of antidepressants

IntroductionPersons using the Internet generate large amounts of health-related data, which are increasingly used in modern health sciences.Objectives/aimsWe analysed the relation between annual prescription volumes (APV) of several antidepressants with marketing approval in Germany and corresponding web search query data generated in Google to test, if web search query volume may be a proxy for medical prescription practice.MethodsWe obtained APVs of several antidepressants related to corresponding prescriptions at the expense of the statutory health insurance in Germany from 2004–2013. Web search query data generated in Germany and related to defined search-terms (active substance or brand name) were obtained with Google Trends. We calculated correlations (Pearson's r) between the APVs of each substance and the respective annual “search share” values; coefficients of determination (R2) were computed to determine the amount of variability shared by the two variables.ResultsSignificant and strong correlations between substance-specific APVs and corresponding annual query volume were found for each substance during the observational interval: agomelatine (r = 0.968; R2 = 0.932; P = 0.01), bupropion (r = 0.962; R2 = 0.925; P = 0.01), citalopram (r = 0.970; R2 = 0.941; P = 0.01), escitalopram (r = 0.824; R2 = 0.682; P = 0.01), fluoxetine (r = 0.885; R2 = 0.783; P = 0.01), paroxetine (r = 0.801; R2 = 0.641; P = 0.01), and sertraline (r = 0.880; R2 = 0.689; P = 0.01).ConclusionsAlthough the used data did not allow to perform an analysis with a higher temporal resolution our results suggest that web search query volume may be a proxy for corresponding prescription behaviour. However, further studies analysing other pharmacologic agents and prescription data that facilitates an increased temporal resolution are needed to confirm this hypothesis.Disclosure of interestThe authors have not supplied their declaration of competing interest.

Red Roses and Gift Chocolates Are Judged More Positively in the U.S. Near Valentine's Day: Evidence of Naturally Occurring Cultural Priming.

Attitudes are not static, but constructed at the moment of the evaluation, incorporating temporary contextual influences. How do meaningful events that naturally occur within a culture, such as a national holiday, shape evaluative judgments of objects related to the holiday? We focused on evaluations of red roses and gift chocolates, which are everyday objects, but also iconic of Valentine’s Day in the U.S. We reasoned that if cultural events shape evaluations, then roses and chocolates would be evaluated differently near Valentine’s Day. Using a large and diverse U.S. sample, we found that as Valentine’s Day neared, evaluations of roses and chocolates (but not a comparison object) were evaluated more positively. Increases in positivity of roses and chocolates covaried with their increased cultural relevance, as quantified by the volume of web search queries involving these terms. These findings provide a demonstration of naturally occurring cultural priming by which the salience of cultural events shape evaluations.

Big Data, Small Pickings: Predicting the Stock Market with Google Trends

Big data such as Google Trends has stimulated much interest in the use of search query volumes for predicting social, business, and financial market trends. A recent paper by Preis, Moat, and Stanley [2013] claimed that a simple trading strategy using the Google Trends keyword debt powerfully predicts the Dow Jones Industrial Average stock index one week ahead and outperforms the buy-and-hold strategy by a factor of 20. Using the same sample period used by Preis, Moat, and Stanley, we show that debt completely loses its predictive power once look-ahead bias is eliminated. We find a similar result with a more recent sample period, from 2011 to 2016. Search terms that do outperform the buy-and-hold strategy generally have no economic meaning and are most likely spurious.

Estimating Influenza Outbreaks Using Both Search Engine Query Data and Social Media Data in South Korea.

BackgroundAs suggested as early as in 2006, logs of queries submitted to search engines seeking information could be a source for detection of emerging influenza epidemics if changes in the volume of search queries are monitored (infodemiology). However, selecting queries that are most likely to be associated with influenza epidemics is a particular challenge when it comes to generating better predictions.ObjectiveIn this study, we describe a methodological extension for detecting influenza outbreaks using search query data; we provide a new approach for query selection through the exploration of contextual information gleaned from social media data. Additionally, we evaluate whether it is possible to use these queries for monitoring and predicting influenza epidemics in South Korea.MethodsOur study was based on freely available weekly influenza incidence data and query data originating from the search engine on the Korean website Daum between April 3, 2011 and April 5, 2014. To select queries related to influenza epidemics, several approaches were applied: (1) exploring influenza-related words in social media data, (2) identifying the chief concerns related to influenza, and (3) using Web query recommendations. Optimal feature selection by least absolute shrinkage and selection operator (Lasso) and support vector machine for regression (SVR) were used to construct a model predicting influenza epidemics.ResultsIn total, 146 queries related to influenza were generated through our initial query selection approach. A considerable proportion of optimal features for final models were derived from queries with reference to the social media data. The SVR model performed well: the prediction values were highly correlated with the recent observed influenza-like illness (r=.956; P<.001) and virological incidence rate (r=.963; P<.001).ConclusionsThese results demonstrate the feasibility of using search queries to enhance influenza surveillance in South Korea. In addition, an approach for query selection using social media data seems ideal for supporting influenza surveillance based on search query data.

A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data

Recently, studies have used search query volume (SQV) data to forecast a given process of interest. However, Google Trends SQV data comes from a periodic sample of queries. As a result, Google Trends data is different every week. We propose a Dynamic Linear Model that treats SQV data as a representation of an unobservable process. We apply our model to forecast the number of hotel nonresident registrations in Puerto Rico using SQV data downloaded in 11 different occasions. The model provides better inference on the association between the number of hotel nonresident registrations and Google Trends SQV than using Google Trends data retrieved only on one occasion. Furthermore, our model results in more realistic prediction intervals of forecasts. However, compared to simpler models we only find evidence of better performance for our model when making forecasts on a horizon of over 6 months.

Tracking Outbreaks of Febrile Infectious Diseases in India Using Google Trends

Background: Search engine activity has been used as an indicator of disease incidence in developed countries. Non-availability of high quality data on Internet use and disease surveillance has hampered similar studies in developing countries. Objective: To evaluate the correlation between search volumes for “fever” on Google and febrile infectious disease outbreaks reported by the Integrated Disease Surveillance Program. Methods: Data on Google search volume for “fever” between January 2014 and December 2014 from India was downloaded from the Google Trends Insights website. Weekly data on outbreaks and case counts of infectious causes of febrile illnesses (dengue, PUO/fever, chikungunya, and typhoid) were obtained from the Integrated Disease Surveillance Program website. Spearman’s rho was calculated to estimate unadjusted correlation between search activities with weekly disease metrics. Time-series analysis of Google search query volume and disease metrics was done to ascertain whether they shared a common stochastic drift using the two-step Engle-Granger method. Results: The unadjusted correlation was statistically significant between search activity and dengue outbreak (r=0.632, p<0.001) and cases (r=0.673, p<0.001); PUO/fever outbreaks (r=0.315, p=0.026) and cases (r=0.323, p=0.022); chikungunya cases (r= 0.374, p=0.007); and total outbreaks (r=0.581, p<0.001) and cases (r=0.615, p<0.001). The test for cointegration of search activity showed stationarity of residuals with both disease outbreaks (p=0.048) and case counts (p=0.041). Conclusions: There was agreement in the search volume with disease outbreaks and case counts. The time trends of search activity were cointegrated with both disease outbreaks and case counts on a weekly basis. This indicated that search activity and infectious diseases outbreaks and case counts were related. However, owing to the non-representative nature of the disease metrics and non-uniform access and limited use of Internet to seek health information, it would be inappropriate to use search engine query volume to predict or forecast disease outbreaks in the Indian context.

Linking Annual Prescription Volume of Antidepressants to Corresponding Web Search Query Data: A Possible Proxy for Medical Prescription Behavior?

Persons using the Internet to retrieve medical information generate large amounts of health-related data, which are increasingly used in modern health sciences. We analyzed the relation between annual prescription volumes (APVs) of several antidepressants with marketing approval in Germany and corresponding web search query data generated in Google to test whether web search query volume may be a proxy for medical prescription practice. We obtained APVs of several antidepressants related to corresponding prescriptions at the expense of the statutory health insurance in Germany from 2004 to 2013. Web search query data generated in Germany and related to defined search terms (active substance or brand name) were obtained with Google Trends. We calculated correlations (Person's r) between the APVs of each substance and the respective annual "search share" values; coefficients of determination (R) were computed to determine the amount of variability shared by the 2 variables. Significant and strong correlations between substance-specific APVs and corresponding annual query volumes were found for each substance during the observational interval: agomelatine (r = 0.968, R = 0.932, P = 0.01), bupropion (r = 0.962, R = 0.925, P = 0.01), citalopram (r = 0.970, R = 0.941, P = 0.01), escitalopram (r = 0.824, R = 0.682, P = 0.01), fluoxetine (r = 0.885, R = 0.783, P = 0.01), paroxetine (r = 0.801, R = 0.641, P = 0.01), and sertraline (r = 0.880, R = 0.689, P = 0.01). Although the used data did not allow to perform an analysis with a higher temporal resolution (quarters, months), our results suggest that web search query volume may be a proxy for corresponding prescription behavior. However, further studies analyzing other pharmacologic agents and prescription data that facilitate an increased temporal resolution are needed to confirm this hypothesis.

Can Internet Search Queries Help to Predict Stock Market Volatility?

AbstractWe study the dynamics of stock market volatility and retail investors' attention to the stock market. The latter is measured by internet search queries related to the leading stock market index. We find a strong co‐movement of the Dow Jones' realised volatility and the volume of search queries for its name. Furthermore, search queries Granger‐cause volatility: a heightened number of searches today is followed by an increase in volatility tomorrow. Including search queries in autoregressive models of realised volatility improves volatility forecasts in‐sample, out‐of‐sample, for different forecasting horizons, and in particular in high‐volatility phases.

Oil price volatility and oil-related events: An Internet concern study perspective

Oil-related events have increased the uncertainty and complexity of the worldwide oil market. This paper investigates the effects of four types of oil-related events on world oil prices, using an event study methodology and an AR-GARCH model. The Internet information concerning these events, which is derived from search query volumes in Google, is introduced in an analytical framework to identify the magnitude and significance of the market response to oil-related events. The results indicate that world oil prices responding to different oil-related events display obvious differentiation. The cumulative abnormal returns, which reflect the influence of the global financial crisis, tend to drop first and then reverse and rise, while the cumulative abnormal returns induced by other oil-related events present a stronger persistent effect. The impact of the global financial crisis on oil price returns is significantly negative, while the impact of the Libyan war and hurricanes is significantly positive. However, the reactions of oil price returns to different OPEC production announcements are inconsistent.

Vaccine-Related Internet Search Activity Predicts H1N1 and HPV Vaccine Coverage: Implications for Vaccine Acceptance

The Internet is a primary source for health-related information, and Internet search activity is associated with infectious disease outbreaks. The authors hypothesized that Internet search activity for vaccine-related information would predict vaccination coverage. They examined Internet search activity for H1N1 and human papilloma virus (HPV) disease and vaccine information in relation to H1N1 and HPV vaccine uptake. Google Insight for Search was used to assess the volume of Internet search queries for H1N1- and vaccine-related terms in the United States in 2009, the year of the H1N1 pandemic. Vaccine coverage data were also obtained from the Centers for Disease Control and Prevention at the state level for H1N1 vaccinations in 2009. These same measures were collected at the state level for HPV- and vaccine-related search terms in 2010 as well as HPV vaccine uptake in that year. Analyses showed that the search terms H1N1 and vaccine were correlated with H1N1 vaccine uptake; ordinal regression found the H1N1 search term was independently associated with H1N1 vaccine coverage. Similarly, the correlation between vaccine search volume and HPV coverage was significant; ordinal regression showed the search term vaccine independently predicted HPV vaccination coverage. This is among the first studies to show that Internet search activity is associated with vaccination coverage. The Internet should be exploited as an opportunity to dispel vaccine misinformation by providing accurate information to support vaccine decision making.