Search Query Data Research Articles

The Rising Interest in Canthoplasty: An Analysis of Online Search Trends.

Canthoplasty as a cosmetic procedure appears to be on the rise in the West. Online search query data offers a powerful tool for analyzing population trends, including changes in patient interest in surgical procedures. Cosmetic surgeons can utilize the internet to increase patient education and interest, as well as to provide information and address misinformation. In this study we sought to verify the increase in cosmetic canthoplasty, for the first time, through analysis of Internet search data, and to establish trends in the interest of Internet users for cosmetic canthoplasty. These trends were subsequently compared with trends in literature publication to establish whether there is a correlation between patient and surgeon interest in the procedure.

Will you go where you search? A deep learning framework for estimating user search-and-go behavior

Every day, people are using search engines for different purposes such as research, shopping, or entertainment. Among the behaviors of search engine users, we are particularly interested in search-and-go behavior, which intuitively corresponds to a simple but challenging question, i.e., will users go where they search? Accurately estimating such behavior can be of great importance for Internet companies to recommend point-of-interest (POI), advertisement, and route, as well as for governments and public service operators like metro companies to conduct traffic monitoring, crowd management, and transportation scheduling. Therefore, in this study, we first collect search log data and GPS log data with linked and consistent user ID from Yahoo! Japan portal application installed in millions of smart-phones and tablets. Then we propose a framework including a complete data-processing procedure and an end-to-end deep learning model to predict whether a user will check-in the searched place or not. Specifically, as users’ daily activities are considered to have high correlation with their travel, eating, and recreation decision in the future (i.e., go or not), Deep Spatial–Temporal Interaction Network (DeepSTIN) is elaborately designed to automatically learn the sophisticated spatiotemporal interactions between mobility data and search query data. Experimental results based on the standard metrics demonstrate that our proposed framework can achieve satisfactory performances on multiple real-world search scenarios.

Google Trends reveals increases in internet searches for insomnia during the 2019 coronavirus disease (COVID-19) global pandemic

The 2019 coronavirus disease (COVID-19) has become a global health and economic crisis. Recent evidence from small samples suggest that it has increased mood and sleep disturbances, including insomnia, around the world. This study aimed to estimate the effect of COVID-19 on insomnia levels worldwide and in the United States during the acute phase of the pandemic. We analyzed search query data recorded between January 2004 and May 2020 from Google Trends and Google Keyword Planner for the search term "insomnia". The number of search queries for insomnia has increased over the past decade and is greater than the number of search queries for other major sleep disorders. The COVID-19 pandemic increased search queries for insomnia both worldwide and in the United States, with the number in the United States increasing by 58% during the first 5 months of 2020 compared with the same months from the previous 3 years. There is a robust diurnal pattern in insomnia search queries in the United States, with the number of queries peaking around 3 am and the overall pattern remaining stable during the pandemic. These results highlight the impact the COVID-19 pandemic has had on sleep health and the urgent need for making effective interventions accessible. Future studies will be needed to determine whether the increase in insomnia symptoms will persist and lead to higher rates of chronic insomnia in the population.

Forecasting Chinese cruise tourism demand with big data: An optimized machine learning approach

Abstract After more than ten years of exponential development, the growth rate of cruise tourist in China is slowing down. There is increasingly financial risk of investing in homeports, cruise ships and promotional activities. Therefore, forecasting Chinese cruise tourism demand is a prerequisite for investment decision-making and planning. In order to enhance forecasting performance, a least squares support vector regression model with gravitational search algorithm (LSSVR-GSA) is proposed for forecasting cruise tourism demand with big data, which are search query data (SQD) from Baidu and economic indexes. In the proposed model, hyper-parameters of the LSSVR model are optimized with GSA. By comparing these models with various settings, we find that LSSVR-GSA with selected mobile keywords and economic indexes can achieve the highest forecasting performance. The results indicate the proposed framework of the methodology is effective and big data can be helpful predictors for forecasting Chinese cruise tourism demand.

A Study On The Influencing Factors Of Tourism Demand From Mainland China To Hong Kong

Tourism research increasingly uses search query data to forecast demand, but the literature rarely explores the mechanisms of the factors influencing demand. A time-varying parameter factor vector auto-regression model is constructed based on Baidu Index on six aspects (dining, shopping, transportation, tours, attractions, and lodging) of tourism demand from January 2011 to March 2019. The model can quantitatively and comprehensively analyze the mechanisms of tourism demand and its six important influencing factors, and can provide suggestions for subsequent planning, construction, and services in the tourism industry. The empirical results show that the relationship between the six factors and domestic tourism demand is time-varying. Dining, attractions, and shopping have a driving effect on tourism demand, and are thus stimulative factors; transportation, tours, and lodging hinder the growth of tourism demand, and are thus baffle factors.

Women’s Intrasexual Competition Results in Beautification

Psychology research focuses primarily on male competition. This research, however, investigates women’s competition for love and the ideal partner in the mating market and reveals one psychological consequence for women, that is, beautification. This is demonstrated with ecologically valid, real-world archive and online search query data, a quasi-experiment, and a series of controlled experiments with random assignments. Intrasexual competition, indexed by the operational sex ratio (OSR) and income inequality (GINI), predicts women’s beautification reflected by Google search queries for cosmetic surgery terms (Study 1) and the density of certificated plastic surgeons (Study 2). Female college students from faculties with female-biased OSRs exhibit greater appearance focus than women from male-biased faculties (Study 3). A causal relationship, between women’s intrasexual competition and beautification (and even self-objectification), is subsequently demonstrated in experiments (Studies 4–6). Additionally, self-objectification due to intrasexual competition leads to women’s preference for appearance-oriented products (Study 6). Implications are discussed.

Machine Learning in Internet Search Query Selection for Tourism Forecasting

Prior studies have shown that Internet search query data have great potential to improve tourism forecasting. As such, selecting the most relevant information from large amounts of search query data is crucial to enhancing forecasting accuracy and reducing overfitting; however, such feature selection methods have not been considered in the tourism forecasting literature. This study employs four machine learning–based feature selection methods to extract useful search query data and construct relevant econometric models. We examined the proposed methods based on monthly forecasting of tourist arrivals in Beijing, China, along with weekly forecasting of hotel occupancy in the city of Charleston, South Carolina, USA. Our findings indicate that the forecasting model with the selected search keywords outperformed the benchmark ARMAX model without feature selection in forecasting tourism demand and hotel occupancy. Therefore, machine learning methods can identify the most useful search query data to significantly improve forecasting accuracy in tourism and hospitality.

Google Trends-based non-English language query data and epidemic diseases: a cross-sectional study of the popular search behaviour in Taiwan

ObjectiveThis study developed a surveillance system suitable for monitoring epidemic outbreaks and assessing public opinion in non-English-speaking countries. We evaluated whether social media reflects social uneasiness and fear during epidemic...

Opportunities and Challenges for Developing Syndromic Surveillance Systems for the Detection of Social Epidemics.

This commentary explores the potential and challenges of developing syndromic surveillance systems with the ability to more rapidly detect epidemics of addiction, poverty, housing instability, food insecurity, social isolation and other social determinants of health (SDoH). Epidemiologists tracking SDoH heavily rely on expensive government surveys released annually, delaying for months if not years the timely detection of social epidemics, defined as sudden, rapid or unexpected changes in social determinants of population health. Conversely, infectious disease syndromic surveillance is an effective early warning tool for epidemic diseases using various types of non-traditional epidemiological data from emergency room chief complaints to search query data. Based on such experience, novel social syndromic surveillance systems for early detection of social epidemics with health implications are not only possible but necessary. Challenges to their widespread implementation include incorporating disparate proprietary data sources and database integration. Significantly more resources are critically needed to address these barriers to allow for accessing, integrating and rapidly analyzing appropriate data streams to make syndromic surveillance for social determinants of health widely available to public health professionals.

Using web search queries to monitor influenza-like illness: an exploratory retrospective analysis, Netherlands, 2017/18 influenza season.

BackgroundDespite the early development of Google Flu Trends in 2009, standards for digital epidemiology methods have not been established and research from European countries is scarce.AimIn this article, we study the use of web search queries to monitor influenza-like illness (ILI) rates in the Netherlands in real time.MethodsIn this retrospective analysis, we simulated the weekly use of a prediction model for estimating the then-current ILI incidence across the 2017/18 influenza season solely based on Google search query data. We used weekly ILI data as reported to The European Surveillance System (TESSY) each week, and we removed the then-last 4 weeks from our dataset. We then fitted a prediction model based on the then-most-recent search query data from Google Trends to fill the 4-week gap (‘Nowcasting’). Lasso regression, in combination with cross-validation, was applied to select predictors and to fit the 52 models, one for each week of the season.ResultsThe models provided accurate predictions with a mean and maximum absolute error of 1.40 (95% confidence interval: 1.09–1.75) and 6.36 per 10,000 population. The onset, peak and end of the epidemic were predicted with an error of 1, 3 and 2 weeks, respectively. The number of search terms retained as predictors ranged from three to five, with one keyword, ‘griep’ (‘flu’), having the most weight in all models.DiscussionThis study demonstrates the feasibility of accurate, real-time ILI incidence predictions in the Netherlands using Google search query data.

Absolutist Words From Search Volume Data Predict State-Level Suicide Rates in the United States

Suicide continues to be a major public health issue, especially in the United States. It is a well-established fact that depression and suicidal ideation are risk factors for suicide. Drawing on recent research that shows that absolutist words (e.g., “completely,” “totally”) constitute linguistic markers of suicidal ideation, we created an online index of absolutist thinking (ATI) using search query data (i.e., Google Trends time series). Mixed-model analyses of age-adjusted suicide rates in the United States from 2004 to 2017 revealed that ATI is linked with suicides, β = 0.22, 95% CI = [0.12, 0.31], p < .001, and predicts suicides within 1 year, β = 0.16, 95% CI = [0.05, 0.28], p = .006, independently of state characteristics and historical trends. It is the first time that a collective measure of absolutist thinking is used to predict real-world suicide outcomes. Therefore, the present study paves the way for novel research avenues in clinical psychological research.

The Relationship Exploration between Public Migration Attention and Population Migration from a Perspective of Search Query.

Rapid population migration has been viewed as a critical factor impacting urban network construction and regional sustainable development. The supervision and analysis of population migration are necessary for guiding the optimal allocation of urban resources and for attaining the high efficiency development of region. Currently, the explorations of population migration are often restricted by the limitation of data. In the information era, search engines widely collect public attention, implying potential individual actions, and freely provide open, timelier, and large-scope search query data for helping explore regional phenomena and problems. In this paper, we endeavor to explore the possibility of adopting such data to depict population migration. Based on the search query from Baidu search engine, three migration attention indexes (MAIs) are constructed to capture public migration attention in cyber space. Taking three major urban agglomerations in China as case study, we conduct the correlation analysis among the cyber MAIs and population migration in geographical space. Results have shown that external-MAI and local-MAI can positively reflect the population migration inner regions and across regions from a holistic lens and that intercity-MAI can be a helpful supplement for the delineation of specific population flow. Along with the accumulation of cyber search query data, its potential in exploring population migration can be further reinforced.

Forecasting Tourism Demand with an Improved Mixed Data Sampling Model

Search query data reflect users’ intentions, preferences and interests. The interest in using such data to forecast tourism demand has increased in recent years. The mixed data sampling (MIDAS) method is often used in such forecasting, but is not effective when moving average (MA) dynamics are involved. To investigate the relevance of the MA components in MIDAS models to tourism demand forecasting, an improved MIDAS model that integrates MIDAS and the seasonal autoregressive integrated moving average process is proposed. Its performance is tested by forecasting monthly tourist arrivals in Hong Kong from mainland China with daily composite indices constructed from a large number of search queries using the generalized dynamic factor model. The forecasting results suggest that this new model significantly outperforms the benchmark model. In addition, comparing the forecasts and nowcasts shows that the latter generally outperforms the former.

Forecasting tourism demand with KPCA-based web search indexes

Search query data (SQD) can be helpful in predicting tourism demand by generating web search indexes. However, valuable nonlinear information in SQD may be neglected by researchers. To effectively capture the nonlinear information, we used kernel principal component analysis (KPCA) to extract web search indexes from SQD. Then, several models with KPCA-based web search indexes were developed for tourism demand forecasting. An empirical study was conducted with collected SQD and real data of tourist arrivals at Hong Kong. The results suggest that models with KPCA-based web search indexes are more accurate than other models because of the nonlinear data processing ability of the KPCA and demonstrate that KPCA-based web search indexes can be excellent predictors for tourism demand forecasting.

Can google trends improve sales forecasts on a product level?

ABSTRACT Combining standard time series models with search query data can be helpful in predicting sales. We include the search volume of company as well as product-related keywords provided by Google Trends as new predictors in models to forecast sales on a product level. Using weekly data from January 2015 to December 2016 of two products of the audio company Sennheiser we find evidence that using Google Trends data can enhance the prediction performance of conventional models.

Spatiotemporal patterns of US drought awareness

Drought is a creeping climatological phenomenon with persistent precipitation deficits. Unlike rapid onset natural hazards such as floods and wildfires, the intangible and gradual characteristics of drought cause a lack of social response during the onset. The level of awareness of a local drought increases rapidly through mass media reports and online information searching activities when the drought reaches its peak severity. This high level of local drought awareness drives concerns for water shortage and support for water policy. However, spatiotemporal patterns of national-scale drought awareness have never been studied due to constraints imposed by time-consuming and costly survey data collection and surveys’ limited sample sizes. Here, we present the national-scale study to reveal the spatiotemporal patterns of drought awareness over the contiguous United States (CONUS) using Google Trends data and Principal Component Analysis (PCA). Results show that the first two PC modes can explain 48% (38% for PC1 and 10% for PC2) of the total variance of state-level drought awareness. We find that the PC1 mode relates to a national pattern of drought awareness across the CONUS. The spatiotemporal patterns further imply that residents in the Northeastern US region are the most aware of the emergence of drought, regardless of the geographic location of the occurrence. The results illustrate how big data, such as search query and social media data, can help develop an effective and efficient plan for drought mitigation in the future.

Internet search query data improve forecasts of daily emergency department volume.

Emergency departments (EDs) are increasingly overcrowded. Forecasting patient visit volume is challenging. Reliable and accurate forecasting strategies may help improve resource allocation and mitigate the effects of overcrowding. Patterns related to weather, day of the week, season, and holidays have been previously used to forecast ED visits. Internet search activity has proven useful for predicting disease trends and offers a new opportunity to improve ED visit forecasting. This study tests whether Google search data and relevant statistical methods can improve the accuracy of ED volume forecasting compared with traditional data sources. Seven years of historical daily ED arrivals were collected from Boston Children's Hospital. We used data from the public school calendar, National Oceanic and Atmospheric Administration, and Google Trends. Multiple linear models using LASSO (least absolute shrinkage and selection operator) for variable selection were created. The models were trained on 5 years of data and out-of-sample accuracy was judged using multiple error metrics on the final 2 years. All data sources added complementary predictive power. Our baseline day-of-the-week model recorded average percent errors of 10.99%. Autoregressive terms, calendar and weather data reduced errors to 7.71%. Search volume data reduced errors to 7.58% theoretically preventing 4 improperly staffed days. The predictive power provided by the search volume data may stem from the ability to capture population-level interaction with events, such as winter storms and infectious diseases, that traditional data sources alone miss. This study demonstrates that search volume data can meaningfully improve forecasting of ED visit volume and could help improve quality and reduce cost.

Using big data to predict pertussis infections in Jinan city, China: a time series analysis.

This study aims to use big data (climate data, internet query data and school calendar patterns (SCP)) to improve pertussis surveillance and prediction, and develop an early warning model for pertussis epidemics. We collected weekly pertussis notifications, SCP, climate and internet search query data (Baidu index (BI)) in Jinan, China between 2013 and 2017. Time series decomposition and temporal risk assessment were used for examining the epidemic features in pertussis infections. A seasonal autoregressive integrated moving average (SARIMA) model and regression tree model were developed to predict pertussis occurrence using identified predictors. Our study demonstrates clear seasonal patterns in pertussis epidemics, and pertussis activity was most significantly associated with BI at 2-week lag (rBI= 0.73, p< 0.05), temperature at 1-week lag (rtemp= 0.19, p< 0.05) and rainfall at 2-week lag (rrainfall= 0.27, p< 0.05). No obvious relationship between pertussis peaks and school attendance was found in the study. Pertussis cases were more likely to be temporally concentrated throughout the epidemics during the study period. SARIMA models with 2-week-lagged BI and 1-week-lagged temperature had better predictive performance (βsearch query= 0.06, p= 0.02; βtemp= 0.16, p= 0.03) with large correlation coefficients (r= 0.67, p< 0.01) and low root mean squared error (RMSE) value (r= 3.59). The regression tree model identified threshold values of potential predictors (search query, climate and SCP) for pertussis epidemics. Our results showed that internet query in conjunction with social and climatic data can predict pertussis epidemics, which is a foundation of using such data to develop early warning systems.

The Validity of Google Trends Search Volumes for Behavioral Forecasting of National Suicide Rates in Ireland.

Annual suicide figures are critical in identifying trends and guiding research, yet challenges arising from significant lags in reporting can delay and complicate real-time interventions. In this paper, we utilized Google Trends search volumes for behavioral forecasting of national suicide rates in Ireland between 2004 and 2015. Official suicide rates are recorded by the Central Statistics Office in Ireland. While similar investigations using Google trends data have been carried out in other jurisdictions (e.g., United Kingdom, United Stated of America), such research had not yet been completed in Ireland. We compiled a collection of suicide- and depression-related search terms suggested by Google Trends and manually sourced from the literature. Monthly search rate terms at different lags were compared with suicide occurrences to determine the degree of correlation. Following two approaches based on vector autoregression and neural network autoregression, we achieved mean absolute error values between 4.14 and 9.61 when incorporating search query data, with the highest performance for the neural network approach. The application of this process to United Kingdom suicide and search query data showed similar results, supporting the benefit of Google Trends, neural network approach, and the applied search terms to forecast suicide risk increase. Overall, the combination of societal data and online behavior provide a good indication of societal risks; building on past research, our improvements led to robust models integrating search query and unemployment data for suicide risk forecasting in Ireland.

Search query formation by strategic consumers

Submitting queries to search engines has become a major way for consumers to search for information and products. The massive amount of search query data available today has the potential to provide valuable information on consumer preferences. In order to unlock this potential, it is necessary to understand how consumers translate their preferences into search queries. Strategic consumers should attempt to maximize the information content of the search results, conditional on a set of beliefs on how the search engine operates. We show using field data that optimal queries may exclude some of the terms that are more relevant to the consumer, potentially at the expense of less relevant terms. In two incentive-aligned lab experiments, we find that consumers have some ability to strategically omit relevant terms when forming their search queries, but that their search queries tend to be suboptimal. In a third incentive-aligned experiment, we find that consumers’ beliefs on how the search engine operates tend to be inaccurate. Overall, our results are consistent with consumers being strategic when formulating their queries, but acting on incorrect beliefs on how the search engine operates.