Google Search Data Research Articles

Measles and Pertussis outbreaks in England and Wales: a time-series analysis

Background Vaccine coverage for common infectious diseases such as Measles and Pertussis (also known as whooping cough) have been declining in England and Wales since 2014. Consequently, significant increases in Measles and Pertussis cases are observed in the community. Aim To explore whether Google Trends offers a predictive utility as a health surveillance tool for Meases and Pertussis in England and Wales. Design and Setting Google search data related to Measles and Pertussis, including common associated symptoms, were downloaded for 52 weeks from 07/01/2023 – 07/01/2024. Measles and Pertussis case data were retrieved from the weekly Notification of Infectious Disease (NOID) reports. Methods The associations between searching and case data were explored using a time-series analyses, including cross-correlations, Prais-Winsten regression and joinpoint analysis. Results Significant cross-correlations were found for Measles cases and “measles” searching (r=.41) at a lag of -1 week. For Pertussis cases, searching for “whooping cough” (r=.31), “cough” (r=.39), “100 day cough” (r.41) and “vomiting” (r=.42) were significantly correlated at a lag of -3 to -2 weeks. In multivariable regression, “measles” remained significantly associated with Measles cases (β=.24, SE=.33, p=.02) as did “whooping cough” (β=.71, SE=.27, p=.01) and “cough” (β=1.99, SE=.54, p=.001) for pertussis. Conclusion Increases in Measles and Pertussis cases follow increases in online searches for both diseases and selected respective symptoms. Further work is required to explore how GT can be used in conjunction with other health surveillance systems to monitor or even predict disease outbreaks, to better target public health interventions.

Disinformation and the Ghost of Margaret Sanger

This study sought to investigate the prominence of U.S. birth control pioneer and eugenicist Margaret Sanger in social media discourse through a critical disinformation studies lens. Using computational and qualitative analysis techniques, 60 months of public Facebook posts and Google search data were analyzed to explore the scope, reach, and engagement with messages that reference Sanger and examine how injustice frames utilizing Sanger’s historical ties to eugenics are being used today to misinform and disinform on abortion and other issues. Injustice frames featuring Sanger were deployed to portray abortion as a method for Black genocide; vilify feminism as racist, anti-woman, and anti-motherhood; and depict the Black Lives Matter social justice movement and cancel culture as hypocritical. This analysis also captured a moment in time when Sanger’s legacy was being reconsidered by Planned Parenthood, the organization she founded, indicating that a long-sought-after reckoning with the racist roots of the early birth control movement in the United States may be underway—with both predictable and unpredictable rippling effects in disinformative spaces.

From crisis to prevention: mining big data for public health insights during the flint water crisis

This study investigates the utility of Google Trends and Google Search data in retrospectively analyzing the Flint Water Crisis, a significant public health event. By examining keywords relevant to the crisis, such as “lead,” “bottled water,” “water filters,” “pneumonia,” and “Legionnaires’ Disease,” we aimed to uncover patterns in public awareness and response during a public crisis and explore how such awareness and responses could potentially aid in reducing the risk of similar crises in the future. Our analysis reveals a clear correlation between search frequencies and the crisis timeline, with spikes in search terms corresponding to key events. This suggests that such data can serve as a valuable tool for understanding public sentiment and behavior in the face of environmental disasters. The research underscores the potential of “Big Data,” led by search engines and social media platforms, in shaping public policy and informed decision-making. However, it also addresses the limitations and challenges in using these data sources, including issues of data consistency, replicability, and the influence of sociocultural contexts on search behaviors. The study advocates for the combined use of Google Trends and Google Search data, complemented by other datasets, for a more comprehensive understanding of public engagement in environmental crises. This work contributes to the growing field of infodemiology, emphasizing the importance of big data analysis in environmental science and public health research.

Integration of Google Trends Data in Predicting New HIV Cases in Western Visayas, the Philippines

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

Digital Trace Data and Demographic Forecasting: How Well Did Google Predict the US COVID‐19 Baby Bust?

AbstractAt the onset of the first wave of COVID‐19 in the United States, the pandemic's effect on future birthrates was unknown. In this paper, we assess whether digital trace data—often touted as a panacea for traditional data scarcity—held the potential to accurately predict fertility change caused by the COVID‐19 pandemic in the United States. Specifically, we produced state‐level, dynamic future predictions of the pandemic's effect on birthrates in the United States using pregnancy‐related Google search data. Importantly, these predictions were made in October 2020 (and revised in February 2021), well before the birth effect of the pandemic could have possibly been known. Our analysis predicted that between November 2020 and February 2021, monthly United States births would drop sharply by approximately 12 percent, then begin to rebound while remaining depressed through August 2021. While these predictions were generally accurate in terms of the magnitude and timing of the trough, there were important misses regarding the speed at which these reductions materialized and rebounded. This ex post evaluation of an ex ante prediction serves as a powerful demonstration of the “promise and pitfalls” of digital trace data in demographic research.

The relationship between online searches and suicide.

In this study, we examined the relationship between 131 suicide related Google search terms, grouped into nine categories, and the number of suicide cases per month in Ecuador from January 2011 to December 2021. First, we applied time-series analysis to eliminate autocorrelation and seasonal patterns to prevent spurious correlations. Second, we used Pearson's correlation to assess the relationship between Google search terms and suicide rates. Third, cross-correlation analysis was used to explore the potential delayed effects between these variables. Fourth, we extended the correlation and cross-correlation analyses by three demographic characteristics - gender, age, and region. Significant correlations were found in all categories between Google search trends and suicide rates in Ecuador, with predominantly positive and moderate correlations. The terms 'stress' (.548), 'prevention' (.438), and 'disorders' (.435) showed the strongest associations. While global trends indicated moderate correlations, sensitivity analysis revealed higher coefficients in men, young adults, and the Highlands region. Specific patterns emerged in subgroups, such as 'digital violence' showing significant correlations in certain demographics, and 'trauma' presenting a unique temporal pattern in women. In general, cross correlation analysis showed an average negative correlation of -.191 at lag 3. Google search data do not provide further information about users, such as demographics or mental health records. Hence, our results are simply correlations and should not be interpreted as causal effects. Our findings highlight a need for tailored suicide prevention strategies that recognize the complex dynamics of suicide risk across demographics and time periods.

Google Search Data Reveals Gaps in Information Transparency and Patient Counseling for Mohs Micrographic Surgery.

Google Search Data Reveals Gaps in Information Transparency and Patient Counseling for Mohs Micrographic Surgery.

Labor market forecasting in unprecedented times: A machine learning approach

AbstractThe COVID‐19 pandemic ushered in unprecedented social and economic conditions, alongside unexpected policy responses, challenging the effectiveness of traditional labor market forecasting approaches. This article presents a novel approach that integrates macroeconomic variables, traditional labor market metrics, and Google search data to develop a machine learning‐based indicator for the Colombian labor market. We employ support vector machine for regression and neural networks models to forecast monthly employment and unemployment rates, explicitly focusing on the third wave of COVID‐19 in the first half of 2021. Our study's findings reveal that the proposed models outperform the autoregressive benchmark regarding forecast accuracy, demonstrating a rapid adaptation to labor market shifts.

Geologic limitations on a comprehensive Anthropocene

Following its advent as a concept, the Anthropocene has flourished as a new worldview in academic and public communities alike. Yet, the Subcommission on Quaternary Stratigraphy recently voted against formally ratifying the Anthropocene as an Epoch, pointing to the restrictive geologic framework, and highlighting the need for a globally represented and synchronous marker. In this Viewpoint, we utilize global bibliographic data, Google search data, IPCC authors, and the Global Climate Risk Index (GCRI) to examine the global diversity of the Anthropocene discourse by measuring academic interest and public engagement in the topic, while also considering the proportional influence, justice, and inclusivity of this discourse. We argue that the conceptual and geological frameworks of the Anthropocene share a complex and non-translatable relationship. Further, we urge reconsideration of geologically rigid definitions in the spirit of recognizing holistic identification of human impacts to the Earth System, while also addressing the gaps in global influence with this pressing concept. Last, we explore successful examples integrating disparate disciplinary perspectives to achieve greater understanding of the Anthropocene and discuss avenues for future directions in the areas of human-environment interactions, as well as environmental justice and equity.

Exploring the Characteristics and Path of Internationalization Development of Vocational Education Based on the Information Diffusion Model

Abstract Internationalization of higher education has become an inevitable trend under the impetus of world economic integration and informationization. This paper adopts the information diffusion technique in the fuzzy information optimization method to construct an assessment model for internationalization development of vocational education. Explicit sample data are turned into fuzzy sets, information diffusion points are built using a two-dimensional average information diffusion model, and the estimated loss and probability distribution of information diffusion are calculated to assess the internationalization development level of vocational education. The constructed indicators set people’s awareness of the internationalization development of vocational education and the dissemination effect, analyzed the internationalization development trend of vocational education in the past five years by combining the Google search data, and explored the impact of internationalization development of vocational education based on the indicators of output level and quality level. The results show that the communication effects of internationalization of vocational education are all rated between (3.3, 3.6), and the attention to the internationalization development of vocational education has increased from 10% to 70% in six years. This study provides suggestions and strategies for advancing the internationalization of higher vocational education in China, which is conducive to steadily increasing the internationalization of higher vocational education in China and improving the level of schooling.

The influence of the Ukraine-Russia conflict on renewable and fossil energy price cycles

This study investigates the impact of the Ukraine-Russia conflict on energy cycles by extending a business cycle-based approach in the energy sector. We employ the Markov Switching Bayesian Vector Autoregressive (MS-BVAR) model to derive filtered probabilities that represent energy cycles. By utilizing the Russia Economic Policy Uncertainty Index and dummy variables derived from Google search data as conflict indicators, we conduct regression analysis and examine the generalized impulse response function to assess the effects of the conflict on energy cycles. Our research reveals notable differences in renewable and fossil energy price cycles. The renewable price cycle exhibits greater persistence, indicating a limited response to the conflict shock. In contrast, the fossil energy price cycle exhibits more significant and enduring adjustments in high- and low-volatility regimes. These findings highlight the heterogeneous impacts of the Ukraine-Russia conflict on the fossil energy market, indicating potential disruptions in supply chains and market sensitivities. Notably, fossil energy prices demonstrate higher responsiveness to the Ukraine-Russia conflict across market conditions.

The evolution of the COVID-19 pandemic through the lens of google searches

Real-time data is essential for policymakers to adapt to a rapidly evolving situation like the COVID-19 pandemic. Using data from 221 countries and territories, we demonstrate the capacity of Google search data to anticipate reported COVID-19 cases and understand how containment policies are associated with changes in socioeconomic indicators. First, search interest in COVID-specific symptoms such as “loss of smell” strongly correlated with cases initially, but the association diminished as COVID-19 evolved; general terms such as “COVID symptoms” remained strongly associated with cases. Moreover, trends in search interest preceded trends in reported cases, particularly in the first year of the pandemic. Second, countries with more restrictive containment policies experienced greater search interest in unemployment and mental health terms after policies were implemented, indicating socio-economic externalities. Higher-income countries experienced a larger increase in searches related to unemployment and a larger reduction in relationship and family planning keywords relative to lower-income countries. The results demonstrate that real-time search interest can be a valuable tool to inform policies across multiple stages of the pandemic.

Nowcasting the nowcasting - Forecasting ISM Business surveys (PMI and NSI) with weekly Google trends

ABSTRACT Changes in economic conditions can occur suddenly with drastic effects. However, economic statistics are published with significant lags, e.g. GDP, and more timely information about the economy is required. Nowcasting methods have become widely popular for providing up-to-date information about the current economic stance. This study adds a novel idea to the previous literature by nowcasting the nowcasting, i.e. the purchasing manager’s index (PMI) and the non-manufacturing survey index (NSI) of the ISM Business survey indicators with the weekly Google Trends data. We used two-dimension reduction methods: the principal component analysis (PCA) and partial least squares (PLS) to eliminate ‘the curse of dimensionality’. Pseudo-out-of-sample exercises performed with different Google Trends search categories indicated that Google Search data is able to generate useful information to nowcast the nowcasting. In particular, we contribute the existing literature that weekly Google Search data can nowcast the monthly PMI and NSI.

Association Between Prosuicide Website Searches Through Google and Suicide Death in the United States From 2010 to 2021: Lagged Time-Series Analysis.

The rate of suicide death has been increasing, making understanding risk factors of growing importance. While exposure to explicit suicide-related media, such as description of means in news reports or sensationalized fictional portrayal, is known to increase population suicide rates, it is not known whether prosuicide website forums, which often promote or facilitate information about fatal suicide means, are related to change in suicide deaths overall or by specific means. This study aimed to estimate the association of the frequency of Google searches of known prosuicide web forums and content with death by suicide over time in the United States, by age, sex, and means of death. National monthly Google search data for names of common prosuicide websites between January 2010 and December 2021 were extracted from Google Health Trends API (application programming interface). Suicide deaths were identified using the CDC (Centers for Disease Control and Prevention) National Vital Statistics System (NVSS), and 3 primary means of death were identified (poisoning, suffocation, and firearm). Distributed lag nonlinear models (DLNMs) were then used to estimate the lagged association between the number of Google searches on suicide mortality, stratified by age, sex, and means, and adjusted for month. Sensitivity analyses, including using autoregressive integrated moving average (ARIMA) modeling approaches, were also conducted. Months in the United States in which search rates for prosuicide websites increased had more documented deaths by intentional poisoning and suffocation among both adolescents and adults. For example, the risk of poisoning suicide among youth and young adults (age 10-24 years) was 1.79 (95% CI 1.06-3.03) times higher in months with 22 searches per 10 million as compared to 0 searches. The risk of poisoning suicide among adults aged 25-64 was 1.10 (95% CI 1.03-1.16) times higher 1 month after searches reached 9 per 10 million compared with 0 searches. We also observed that increased search rates were associated with fewer youth suicide deaths by firearms with a 3-month time lag for adolescents. These models were robust to sensitivity tests. Although more analysis is needed, the findings are suggestive of an association between increased prosuicide website access and increased suicide deaths, specifically deaths by poisoning and suffocation. These findings emphasize the need to further investigate sites containing potentially dangerous information and their associations with deaths by suicide, as they may affect vulnerable individuals.

Measuring environmental awareness: An analysis using google search data

Environmental awareness is usually measured using surveys. This paper aims to offer an alternative measure: an Environmental Awareness Index (EAI) constructed using Google search data provided by Google Trends. The benefits of using Google search data over surveys are that (i) they are less costly to obtain, (ii) they are available at high frequency, and (iii) they cover countries where no surveys are available. To test the validity of the proposed EAI, this study empirically assesses the impact of the computed index on individuals' pro-environmental behaviors using the Special Eurobarometer: Attitudes of European citizens towards the Environment data. Results show that the EAI is positively related to pro-environmental behaviors with a statistical significance at the one percent level. This finding stays robust in pooled OLS as well as in panel regression analysis when GDP, mean years of schooling, and population are included as control variables and when time-fixed effects are introduced. Further, the results confirm that environmental awareness is not stable over time and underline the importance of having a timely measure of environmental awareness at hand. Finally, the findings offer several practical implications for managers and policymakers, who will be able to use a timely measure of environmental awareness, assess and measure the impact of their policies aiming to raise environmental awareness as well as depict the type of behavior influenced by their policies.

A Model Selection Approach for Time Series Forecasting: Incorporating Google Trends Data in Australian Macro Indicators.

This study examined whether the behaviour of Internet search users obtained from Google Trends contributes to the forecasting of two Australian macroeconomic indicators: monthly unemployment rate and monthly number of short-term visitors. We assessed the performance of traditional time series linear regression (SARIMA) against a widely used machine learning technique (support vector regression) and a deep learning technique (convolutional neural network) in forecasting both indicators across different data settings. Our study focused on the out-of-sample forecasting performance of the SARIMA, SVR, and CNN models and forecasting the two Australian indicators. We adopted a multi-step approach to compare the performance of the models built over different forecasting horizons and assessed the impact of incorporating Google Trends data in the modelling process. Our approach supports a data-driven framework, which reduces the number of features prior to selecting the best-performing model. The experiments showed that incorporating Internet search data in the forecasting models improved the forecasting accuracy and that the results were dependent on the forecasting horizon, as well as the technique. To the best of our knowledge, this study is the first to assess the usefulness of Google search data in the context of these two economic variables. An extensive comparison of the performance of traditional and machine learning techniques on different data settings was conducted to enable the selection of an efficient model, including the forecasting technique, horizon, and modelling features.

Oral and Dental Needs and Teledentistry Applications in the Elderly: Real-Time Surveillance Using Google Trends

Considering the increasing need for oral and dental care in the elderly, teledentistry has been proposed to improve the education of elderly patients in oral health maintenance and risk factor control, identify patients’ concerns in advance, facilitate monitoring, and save time and money. The present cross-sectional analysis of Google search data through real-time surveillance with Google Trends aimed to determine Google users’ interest in oral and dental needs and teledentistry applications in the elderly, and to compare search volumes before and after the COVID-19 outbreak. Extracted CVS data were qualitatively analyzed. Pearson and Spearman correlation analyses were performed between searches for “elderly” and “teledentistry”, and all the oral and dental needs and teledentistry applications. The Mann–Whitney U test compared search volumes in the 36 months before and after the beginning of the COVID-19 pandemic. Google users’ interest in the elderly and related oral and dental needs was diffusely medium–high, while teledentistry and its applications were of lower interest. Interest in teledentistry and its applications was strongly related to interest in the older population, which is consistent with the assumption that older adults represent the population segment that could benefit most from these tools. A positive correlation was also found between searches for “Elderly” and searches for almost all oral and dental needs typical of the geriatric population. Search volumes increased significantly after the outbreak of the COVID-19 pandemic. More information about teledentistry should be disseminated to increase knowledge and awareness, especially among older patients, about its indications, applications, and advantages.

The Global Resonance of Human Rights: What Google Trends Can Tell Us

Where is the human rights discourse most resonant? We use aggregated cross-national Google search data to test two divergent accounts of why human rights appeal to some populations but not others. The top-down model predicts that nationwide interest in human rights is attributable mainly to external factors such as foreign direct investment, transnational NGO campaigns, or international legalization, whereas the bottom-up model highlights the importance of internal factors such as economic growth and persistent repression. We find more evidence for the latter model: not only is interest in human rights more concentrated in the Global South, but the discourse is also most resonant where people face regular state violence. In drawing these inferences, this article confronts high-level debates over whether human rights will remain relevant in the future, and whether the discourse still animates counter-hegemonic modes of resistance. The answer to both questions, our research suggests, is “yes.”

Online Health Information Seeking for Mpox in Endemic and Nonendemic Countries: Google Trends Study.

The recent global outbreak of mpox (monkeypox) has already been declared a public health emergency of international concern by the World Health Organization. Given the health, social, and economic impacts of the COVID-19 pandemic, there is understandable concern and anxiety around the emergence of another infectious disease-especially one about which little is known. We used Google Trends to explore online health information seeking patterns for mpox in endemic and nonendemic countries and investigated the impact of the publication of the first in-country case on internet search volume. Google Trends is a publicly accessible and free data source that aggregates worldwide Google search data. Google search data were used as a surrogate measure of online health information seeking for 178 days between February 18 and August 18, 2022. Searching data were downloaded across this time period for nonendemic countries with the highest case count (United States, Spain, Germany, United Kingdom, and France) and 5 endemic countries (Democratic Republic of Congo, Nigeria, Ghana, Central African Republic, and Cameroon). Joinpoint regression analysis was used to measure changes in searching trends for mpox preceding and following the announcement of the first human case. Online health information seeking significantly increased after the publication of the first case in all the nonendemic countries-United States, Spain, Germany, United Kingdom, and France, as illustrated by significant joinpoint regression models. Joinpoint analysis revealed that models with 3 significant joinpoints were the most appropriate fit for these data, where the first joinpoint represents the initial rise in mpox searching trend, the second joinpoint reflects the start of the decrease in the mpox searching trend, and the third joinpoint represents searching trends' return to searching levels prior to the first case announcement. Although this model was also found in 2 endemic countries (ie, Ghana and Nigeria), it was not found in Central African Republic, Democratic Republic of Congo, or Cameroon. Findings demonstrate a surge in online heath information seeking relating to mpox after the first in-country case was publicized in all the nonendemic countries and in Ghana and Nigeria among the endemic counties. The observed increases in mpox searching levels are characterized by sharp but short-lived periods of searching before steep declines back to levels observed prior to the publication of the first case. These findings emphasize the importance of the provision of accurate, relevant online public health information during disease outbreaks. However, online health information seeking behaviors only occur for a short time period, and the provision of accurate information needs to be timely in relation to the publication of new case-related information.

Crude oil price forecasting with machine learning and Google search data: An accuracy comparison of single-model versus multiple-model

Recent research has shown that introducing online data can significantly improve forecasting ability. This study considers several popular single-model machine learning methods and a stacking multiple-model ensemble learning strategy. These are used with online data from Google Trends to forecast crude oil prices. The study first selects dozens of alternative Google Trends, which may capture crude oil price fluctuations. A co-integration test and Granger causality analysis are used to investigate the effect of Google Trends on crude oil prices. Then, the multiple-model methods are compared with several popular single-model machine learning methods that are used to forecast crude oil prices. These methods are used with Google Trends that have a significant relationship with the crude oil price. Experimental results indicate that introducing Google Trends can improve the forecasting performance; multiple-model methods also outperform several popular single-model machine learning methods in terms of prediction accuracy.