Google Trends Data Research Articles

COVID-19, school closures and (cyber)bullying in Germany

ABSTRACT We analyse the prevalence of bullying in Germany during COVID-19, both as in-person bullying (in our context: school bullying) and via social media and electronic communication tools (cyberbullying). Using Google Trends data from 2013 to 2022 and exploiting the COVID-19 pandemic as a natural experiment when schools switched to distance learning, we document stark changes in the prevalence of (cyber)bullying in Germany: Our results indicate that during school years affected by COVID-19, online searches for school bullying decreased by about 29 percent, while online searches for cyberbullying increased by about 40 percent during the same periods.

Predicting Prefecture-Level Well-Being Indicators in Japan Using Search Volumes in Internet Search Engines: Infodemiology Study.

In recent years, the adoption of well-being indicators by national governments and international organizations has emerged as an important tool for evaluating state governance and societal progress. Traditionally, well-being has been gauged primarily through economic metrics such as gross domestic product, which fall short of capturing multifaceted well-being, including socioeconomic inequalities, life satisfaction, and health status. Current well-being indicators, including both subjective and objective measures, offer a broader evaluation but face challenges such as high survey costs and difficulties in evaluating at regional levels within countries. The emergence of web log data as an alternative source of well-being indicators offers the potential for more cost-effective, timely, and less biased assessments. This study aimed to develop a model using internet search data to predict well-being indicators at the regional level in Japan, providing policy makers with a more accessible and cost-effective tool for assessing public well-being and making informed decisions. This study used the Regional Well-Being Index (RWI) for Japan, which evaluates prefectural well-being across 47 prefectures for the years 2010, 2013, 2016, and 2019, as the outcome variable. The RWI includes a comprehensive approach integrating both subjective and objective indicators across 11 domains, including income, job, and life satisfaction. Predictor variables included z score-normalized relative search volume (RSV) data from Google Trends for words relevant to each domain. Unrelated words were excluded from the analysis to ensure relevance. The Elastic Net methodology was applied to predict RWI using RSVs, with α balancing ridge and lasso effects and λ regulating their strengths. The model was optimized by cross-validation, determining the best mix and strength of regularization parameters to minimize prediction error. Root mean square errors (RMSE) and coefficients of determination (R2) were used to assess the model's predictive accuracy and fit. An analysis of Google Trends data yielded 275 words related to the RWI domains, and RSVs were collected for 211 words after filtering out irrelevant terms. The mean search frequencies for these words during 2010, 2013, 2016, and 2019 ranged from -1.587 to 3.902, with SDs between 3.025 and 0.053. The best Elastic Net model (α=0.1, λ=0.906, RMSE=1.290, and R2=0.904) was built using 2010-2016 training data and 2-13 variables per domain. Applied to 2019 test data, it yielded an RMSE of 2.328 and R2 of 0.665. This study demonstrates the effectiveness of using internet search log data through the Elastic Net machine learning method to predict the RWI in Japanese prefectures with high accuracy, offering a rapid and cost-efficient alternative to traditional survey approaches. This study highlights the potential of this methodology to provide foundational data for evidence-based policy making aimed at enhancing well-being.

Significantly Increased Public Interest in Sleep Disorder during the COVID-19 Pandemic: An Analysis of Google Trends Data

Background: The COVID-19 pandemic has had a profound impact on sleep disorders. Previous studies have shown that people's sleep time is delayed. Methods: Analyze the public's interest to the keyword "sleep disorder" during the COVID-19 pandemic from December 2019 to December 2022 using Google Trends. The study also focused on the search trend data of the top three countries with high to low overall search frequency for this keyword. Results: It has been observed that during the peak period of the COVID-19 pandemic, the public's search interest for "sleep disorder" has significantly increased. Analyzing Google Trends from December 2019 to December 2022, the top three countries with high interest in searching for "sleep disorder" are the United States, the Philippines, and Canada. It shows that search interest in the United States is an increasing trend year by year, and the overall trend is relatively stable. The search trend for "sleep disorder" among Filipino netizens fluctuates greatly and is generally on a downward trend. The search trend for "sleep disorder" among Canadian netizens is moderate, and the overall trend is like that of Filipinos. It is worth noting that the Google Trends for "sleep disorder" among Filipino and Canadian netizens has changed from an overall increase to an overall decrease in 2020 as the watershed. This indicates a temporal correlation between the surge in COVID-19 cases and online search for "sleep disorder". Conclusion: It shows that public interest in "sleep disorder" has significantly increased during the COVID-19 pandemic, and there may be a certain correlation between the COVID-19 pandemic and sleep disorders. These are worthy of further exploration by researchers, especially the changes in people's daily routines caused by the COVID-19 pandemic, which in turn affect people's sleep quality.

Analysis and modelling of global online public interest in multiple other infectious diseases due to the COVID-19 pandemic.

Previous research has demonstrated the applicability of Google Trends in predicting infectious diseases. This study aimed to analyze public interest in other infectious diseases before and after the outbreak of COVID-19 via Google Trends data and to predict these trends via time series models. Google Trends data for 12 common infectious diseases were obtained in this study, covering the period from 1 February 2018 to 5 May 2023. The ARIMA, TimeGPT, XGBoost, and LSTM algorithms were then utilized to establish time series prediction models. Our study revealed a significant decrease in public interest in most infectious diseases at the beginning of the pandemic outbreak, followed by a rebound in the post-pandemic era, which is consistent with reported disease incidences. Furthermore, our prediction models demonstrated good accuracy, with TimeGPT showing unique advantages. Our study highlights the potential application value of Google Trends and large pre-trained models for infectious disease prediction.

Automation of time series analysis of Google trends data in R studio using autonomous AI agents

Automation of time series analysis of Google trends data in R studio using autonomous AI agents

Using Google Trends Data to Study High-Frequency Search Terms: Evidence for a Reliability-Frequency Continuum

Google Trends (GT) data are increasingly used in the social sciences and adjacent fields. However, previous research on the quality of GT data has raised concerns regarding their reliability. In the present study, we investigated whether reliability differs between low- and high-frequency search terms. In other words, we explored the existence of a reliability-frequency continuum in GT data. Our study adds to previous research by investigating a more comprehensive set of search terms and different aspects of reliability (e.g., differences in relative search volume distributions, correctly identified maxima). For this purpose, we collected samples of GT data for ten high- and two low-frequency search terms. We obtained one real-time sample and 62 non–realtime samples per search term (30 non–realtime samples for low-frequency search terms). Data collection was restricted to search data for Germany. Our data support the existence of a reliability-frequency continuum—low-frequency search terms are subject to greater reliability issues compared to high-frequency search terms. Based on our findings, we have derived practical recommendations for the use of GT data and have outlined future research opportunities.

Analyzing Public Interest in Geohazards Using Google Trends Data

This study investigates public interest in geological disasters by analyzing Google Trends data from 2023. This research focuses on earthquakes, hurricanes, floods, tornadoes, and tsunamis to understand how search behaviors reflect public awareness and concern. This study identifies temporal and geographical patterns in search trends. Key findings reveal that public interest spikes during significant disaster events, such as the February 2023 earthquake in Turkey and Syria and the August 2023 hurricanes in the United States. This study highlights the importance of timely and accurate information dissemination for disaster preparedness and response. Google Trends proves to be a valuable tool for monitoring public interest, offering real-time insights that can enhance disaster management strategies and improve community resilience. This study’s insights are essential for policymakers, disaster management agencies, and educational efforts aimed at mitigating the impacts of natural disasters.

Changes in Internet Search Behavior After a Direct-to-Consumer Advertising Campaign for Faricimab-svoa.

Purpose: To analyze changes in Google search volume after US Food and Drug Administration (FDA) approval and initiation of a direct-to-consumer marketing campaign for faricimab-svoa. Methods: Google Trends data between December 26, 2021, and June 17, 2023, were downloaded and searched for terms prominently featured in the marketing campaign, including "wet AMD", "diabetic macular edema", "Vabysmo", and "faricimab-svoa". Data were standardized to the week and the term with the highest search volume, resulting in weekly relative search volumes for each term. The mean relative search volume and percentage change in relative search volume were calculated for the time periods of interest. Results: The direct-to-consumer campaign was associated with an increase in relative search volume in the first month for the terms "Vabysmo" (2.5% to 18.0%) and "wet AMD" (3.0% to 77.3%) and was sustained in the second month (P < .01). No significant changes in relative search volume were seen for the terms "diabetic macular edema" or "faricimab-svoa". After FDA approval, "Vabysmo" had the only significant increase in relative search volume (0.3% to 2.8%; P = .02). Conclusions: A direct-to-consumer advertising campaign for faricimab-svoa was associated with a surge in Google search volume of 620% and 2475% for "Vabysmo" and "wet AMD," respectively (P < .01), without a corresponding increase for "diabetic macular edema". Although FDA approval was associated with an increase in search volume for "Vabysmo" (P = .02), the marketing campaign was more influential at driving internet search behavior.

Detecting pyramid scheme activity with Google trends data

ABSTRACT This article explores the relationship between pyramid scheme participation and internet search activity, using a case study of a shuttered pyramid scheme in the United States. Comparing results against the Bass diffusion model, we estimate the relationship between monthly pyramid scheme uptake at the state level and the search frequency of key terms in that state (e.g. the scheme name). Using monthly data from 2004 to 2013, we find evidence that Google Trends data can be an effective complement to other fraud detection methods at the state level. This tool is especially useful in situations where consumer complaints are rare, and it is difficult to detect or measure the level of scheme activity.

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.

Internet searches for ADHD medications surged during the COVID-19 pandemic

On January 2020, the World Health Organization (WHO) declared that Coronavirus Disease (COVID-19) had become a public health emergency of international concern (PHEIC) and was assessed as a pandemic in March 2020. For the next 2–3 years, Americans followed stay-at-home orders, and used virtual technologies, while struggling with pandemic-related stressors (1). This affected mental health (2). Attention-deficit hyperactivity disorder (ADHD) symptoms increased (3), resulting in an uptick in ADHD prescriptions (4). A shortage of Adderall was announced by the U.S. Food and Drug Administration (FDA) in October 2022. As actual prescription usage data were not available on the short time frame of the pandemic, we explored the potential of using internet searches as a proxy for real health behavior, with prevention of future shortages in mind. We used Google Trends (GT) data (5) to estimate public interest in ADHD medications during the pandemic.

Nowcasting of Indonesia's Gross Domestic Product Using Mixed Sampling Data Regression and Google Trends Data

This study aims to compare the results of the GDP nowcasting of the accommodation and food service activities sector without and with the pandemic time using the MIDAS method. The MIDAS method is an econometric approach used to predict economic development using real-time available high-level and low-frequency data. In this study, Google Trend acts as a predictor variable consisting of 16 search categories which are then reduced by Principal Component Analysis, resulting in several principal components. For GDP data, the data period collected is Quarter I 2010 to Quarter I 2023. This period will later be partitioned into the period before the COVID-19 pandemic, namely Quarter I 2010 to Quarter IV 2019 and a combined period, namely Quarter I 2010 to Quarter I of 2023. This partition was carried out to see the performance and sensitivity of the model before and after the shock due to the COVID-19 pandemic. From the models that have been made, nowcasting is carried out and it is found that the RMSE and MAE values for the pre-pandemic model are smaller than the combined model. The RMSE values for each of the pre-pandemic and combined models were 0.005753 and 0.056032 and the MAE values were 0.00359 and 0.048976 for the pre-pandemic and combined models. However, from this study it is not advisable to make predictions on the nominal GDP of the accommodation and food service activities sector because the results of the nowcasting predictions are still far from the actual value, but can be a reference if you want to predict the growth direction of the accommodation and food service activities sector.

Assessing Google Search Trends for Mpox in the United States

Background and Aims: Mpox (formerly Monkeypox) is a zoonotic infectious disease with symptoms of rash, fever, and swollen lymph nodes. The most recent outbreak was reported in May 2022 with the first US case identified in the middle of the same month. Google Trends is a unique tool that quantifies frequencies of a specified search term in real-time, in a given time range, and by geographical location. The present study evaluates the utilisation of Google Trends data in understanding Mpox patterns in the US. Materials and Methods: On 29 September 2023, a Google Trends search was executed to analyse the popularity of the term Mpox for time range of 10 May 2022 to 28 September 2023. Data was exported into MS Excel and matched with the CDC’s Mpox case incidence data. Results: Mpox search was most popular in the District of Columbia (100) followed by Delaware (71), Maryland (70) and Georgia (68), respectively. The popularity scale of 24 was observed 2 days before the first US case was identified which could be attributed to the global rise in cases earlier in May 2022. Although a steep increase in Mpox searches was observed between July and August 2022, the trend aligned with the number of Mpox cases reported to the CDC during the same time frame. Conclusions: Google Trends data, if used in conjunction with other surveillance tools, can help in a better understanding of disease dynamics and may help validate trends obtained through other sources. Since Google Trends monitors and analyses searches in real-time, it can be valuable in establishing a robust action plan.

Well-being effects of the digital platform economy: the case of temporary and self-employment

The surge in non-traditional employment, including self-employment and temporary jobs, driven by the digital platform economy (commonly known as the gig economy), has thrust this form of work into the centre of social and political discussions. Among the European Union countries, Spain stands out with the highest volume of digital platform work. To explore the well-being effects of various gig economy employment arrangements, this study utilizes microdata from the Spanish Living Conditions Survey for 2018 and Google Trends data related to platforms like Deliveroo, Airbnb, Just Eat, Uber, and Freelance as a proxy for digital platform economy demand. Employing an econometric approach based on instrumental variables, the study reveals that the most detrimental well-being effects are observed among self-employed workers. Specifically, in terms of self-reported health, self-employed individuals (own-account workers) exhibit 125.8% lower average health levels compared to permanent workers. These findings suggest that the heightened job insecurity and precariousness associated with self-employment outweigh the potential benefits arising from greater flexibility and autonomy in this type of work.

Google Internet searches related to inflammatory arthritis: An observational study using Google Trends data.

The Internet has transformed how patients access health information. We examined Google search engine data to understand which aspects of health are most often searched for in combination with inflammatory arthritis (IA). Using Google Trends data (2011-2022) we determined the relative popularity of searches for 'patient symptoms' (pain, fatigue, stiffness, mood, work) and 'treat-to-target' (disease-modifying drugs, steroids, swelling, inflammation) health domains made with rheumatoid arthritis (RA), psoriatic arthritis (PsA), and axial spondyloarthritis (AxSpA) in the UK/USA. Google Trends normalises searches by popularity over time and region, generating 0-100 scale relative search volumes (RSV; 100 represents the time-point with most searches). Up to five search term combinations can be compared. In all IA forms, pain was the most popular patient symptom domain. UK/USA searches for pain gave mean RSVs of 58/79, 34/51, and 39/63 with RA, PsA, and AxSpA; mean UK/USA RSVs for other patient symptom domains ranged 2-7/2-8. Methotrexate was the most popular treat-to-target search term with RA/PsA in the UK (mean 28/21) and USA (mean 63/33). For AxSpA, inflammation was most popular (mean UK/USA 9/34). Searches for pain were substantially more popular than searches for methotrexate in RA and PsA, and inflammation in AxSpA. Searches increased over time. Pain is the most popular search term used with IA in Google searches in the UK/USA, supporting surveys/qualitative studies highlighting the importance of improving pain to patients with IA. Routine pain assessments should be embedded within treat-to-target strategies to ensure patient perspectives are considered.

Predicting Tourist Arrivals: A Google Trends-Based Model for Destination Management

In this paper, a review of the scientific literature on the importance and knowledge of the tourist destination for the formation of a correct strategy for its development and promotion is made. The possibility of managing the tourist destination by modeling with Google Trends data is represented. A review of studies using the mentioned tool in the field of tourism is conducted. The potential benefits of using Google Trends data on searches for a city as a keyword in combination with archival data are summarized. The possibilities for modeling the visits of tourists using data from Google Trends are investigated, as a result of which a model is built by applying polynomial regression to predict the overnight stays of foreign citizens in the city of Veliko Tarnovo based on archival data from the National Statistical Institute and Google Trends data. Through data analytics with Google Trends, it is concluded that key value is created for the external and internal environment, influencing the destination. The data creates conditions for the development of management strategies for attracting tourists based on primary and secondary data for analysis. The application of the received data sets through data analysis are also important for tourism organizations and institutional bodies in tourism (macro- and meso-level). The resulting analysis affects issues related to the management and marketing of the destination, and on the other hand, it directly affects the segmentation of the market and the distribution of products and services to the end user (micro level).

Bitcoin Return Prediction: Relevant Factors Determining Return

Cryptocurrencies with Bitcoin being the most well-known, are volatile in price. Being able to predict or speculate price movements in Cryptocurrencies would then be meaningful for researchers and lucrative for investors. This paper hypothesized relevant factors that may determine return of Bitcoin, namely Bitcoin price data, google trend and precious metal price data, and collects data for the past 6 years. After regrouping and processing the data, a regression model is used to test the strengths of predictability of each possible factor. The results reveal that Bitcoin price data is a valid predictor of daily Bitcoin return, google trend or search volume is valid only for weekly return predictions, and precious metal prices do not have a significant relationship with Bitcoin return.

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.

Assessing The Relationship Between Demand And Accessibility For Pediatric Ophthalmology Services By State In The United States.

To investigate the relationship between public demand for pediatric ophthalmology services and the accessibility of such services on a statewide basis in the United States, focusing on strabismus care. Using Google Trends data, search volumes for "strabismus" were analyzed from January 2014 to December 2023. Pediatric ophthalmologist availability was assessed via the American Academy of Ophthalmology directory, normalized for state population size to create a relative demand index. Additional metrics from the United States Census Bureau and Centers for Disease Control and Prevention provided socioeconomic and health data. Correlation coefficients were used to explore relationships between search volumes, specialist availability, vision screening, socioeconomics, and demographic factors. The data revealed variability in public interest in pediatric ophthalmology across states, with Alaska showing the highest relative search volume for strabismus. The data also indicated notable disparities in pediatric ophthalmologist density, with North Dakota, Vermont, and Wyoming having no pediatric ophthalmologists. A significant correlation was identified between pediatric ophthalmologist availability and vision screening (R = 0.486, p < 0.001). Negative correlations were observed between the relative demand index and urbanization (R = -0.388, p = 0.007), as well as the strabismus prevalence (R = -0.455, p = 0.001), indicating that urbanization and specialist distribution influence eyecare demand and access. The findings highlight disparities in accessibility and demand for pediatric ophthalmology services across the US, influenced by urbanization and distribution of healthcare professionals. The study underscores the need for targeted interventions to improve access to care and bridge gaps in pediatric ophthalmology service provision.

Continuing vocational training in times of economic uncertainty: an event-study analysis in real time

Continuing vocational training (CVT) is a key channel for employees to adapt their skills to changing requirements in the labor market due to structural changes and digitization. The COVID-19 pandemic and the energy crisis as a consequence of the war in Ukraine may have accelerated these developments. Yet, it is unclear how the economic impact of these crises affects individuals’ occupational preferences. In this study, we want to investigate how interest in CVT changes in times of economic uncertainty. We use Google Trends data for Germany and apply an event study analysis to examine how interest in CVT developed with the onset of the COVID-19 pandemic and the Russian attack on Ukraine. We find that the interest in CVT strongly declined during the first wave of the pandemic regardless of how severely a region was affected. During the second lockdown, the decline in CVT interest was more pronounced in the eastern German states where we find a general decline in search intensity since March 2020. We also consider different channels that may have influenced the demand for CVT during the pandemic. Overall, we show that during the first 2.5 years of the pandemic, the search intensity for CVT decreased on average by 12 to 19 percent, while the search intensity for online CVT increased by 39 to 45 percent. We also see a decrease in the search intensity for CVT at the beginning of the energy crisis.