Google Trends Search Research Articles

Association Between School-Related Google Trends Search Volume and Suicides Among Children and Adolescents in Japan During 2016-2020: Retrospective Observational Study With a Time-Series Analysis.

Suicide is the leading cause of death among children and adolescents in Japan. Internet search volume may be useful in detecting suicide risk. However, few studies have shown an association between suicides attempted by children and adolescents and their internet search volume. This study aimed to examine the relationship between the number of suicides and the volume of school-related internet searches to identify the search terms that could serve as the leading indicators of suicide prevention among children and adolescents. We used data on weekly suicides attempted by elementary, middle, and high school students in Japan from 2016 to 2020, provided by the National Police Agency. Internet search volume was weekly data for 20 school-related terms obtained from Google Trends. Granger causality and cross-correlation analysis were performed to estimate the temporal back-and-forth and lag between suicide deaths and search volume for the related terms. The search queries "I do not want to go to school" and "study" showed Granger causality with suicide incidences. The cross-correlation analysis showed significant positive correlations in the range of -2 to 2 for "I do not want to go to school" (highest value at time lag 0, r=0.28), and -1 to 2 for "study" (highest value at time lag -1, r=0.18), indicating that the search volume increased as the number of suicides increased. Furthermore, during the COVID-19 pandemic period (January-December 2020), the search trend for "I do not want to go to school," unlike "study," was highly associated with suicide frequency. Monitoring the volume of internet searches for "I do not want to go to school" could be useful for the early detection of suicide risk among children and adolescents and for optimizing web-based helpline displays.

From Queries to Transformations: A Correlation Analysis of Google Trends Search Data of Cosmetic Dermatologic Surgery.

From Queries to Transformations: A Correlation Analysis of Google Trends Search Data of Cosmetic Dermatologic Surgery.

Google Search Trends About Systemic Psoriasis Treatment: What Do People Want to Know About Biologics and Janus Kinase Inhibitors?

This study investigates the most frequently asked questions regarding systemic treatments for psoriasis vulgaris using Google Trends and People Also Asked (PAA) tools, focusing on traditional DMARDs, biologics, and small-molecule inhibitors. Analyzing data from January 31, 2019, to January 31, 2024, Adalimumab was found to have the highest search volume, followed by Apremilast and Methotrexate in their respective categories. Based on Rothwell's Question Classification Criteria, questions about Adalimumab predominantly centered around their technical, factual details and cost of the medication. The study highlights significant public interest in these factors over their safety profile and it is likely influenced by Adalimumab's high direct-to-consumer marketing. Moreover, evaluation of 782 websites showed that potential patients were seeking commercial and government-based websites to learn more about systemic psoriasis treatment. According to the JAMA benchmark criteria, these source types also scored the highest in terms of information quality, while medical practice websites scored the lowest. The findings suggest the need for dermatologists to address factual details such as medication administration scheduling, dietary restrictions, and cost during patient consultations, and to guide patients towards high-quality online resources. Limitations include potential comprehensiveness of questions and evolving medication concerns.

Identifying Global Awareness of SGLT2 Inhibitors through Google Search Trend Analysis

Identifying Global Awareness of SGLT2 Inhibitors through Google Search Trend Analysis

Identifying Global Awareness of Glucagon-Like Peptide 1 Receptor Agonists (GLP-1 RAs) through Google Search Trend Analysis

Identifying Global Awareness of Glucagon-Like Peptide 1 Receptor Agonists (GLP-1 RAs) through Google Search Trend Analysis

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.

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.

Zoom Boom or Bust? Understanding Post-Pandemic Interest in Facial Plastic Surgery via Google Trends.

To understand post-pandemic interest in plastic surgery procedures via Joinpoint analysis of Google Trends search data. Google Trends was used to quantify search volumes from January 2019-December 2022 for select cosmetic face and body procedures in the United States. A keyword analytic tool (Keywords Everywhere) extracted absolute search volumes (average monthly searches). Joinpoint analysis assessed search trends over time reported as monthly percentage change (MPC). All procedures queried, including a non-cosmetic control (cataract surgery), demonstrated expected declines at the start of the COVID-19 pandemic. Blepharoplasty, face lift, neck lift, and Botox demonstrated statistically significant increase in search volumes that remained elevated relative to pre-pandemic levels. Rhinoplasty, fillers, and abdominoplasty interest increased initially followed by return to pre-pandemic levels by the end of 2022. The remainder of search terms did not show a clear temporal associated with COVID-19 lockdowns. The "Zoom Boom" appears to be a real phenomenon reflected by sustained increase in public interest in relation to facial plastic procedures. NA Laryngoscope, 2024.

Election Interference and Online Propaganda Campaigns: Dynamic Interdependencies on Facebook, Google Trends, and the New York Times

The relationship between propaganda campaigns, news outlets, and search patterns is of significant interest to political authorities and academic scholars from various disciplines. We explore these dynamic relationships using 3,500 Facebook propaganda advertisements, 167,000 New York Times stories, and hundreds of Google Trends searches for terms from the advertisements and articles in the two years preceding the 2016 US presidential election. The data indicate that propaganda campaigns utilize random content infrequently and instead follow specific Google search patterns. Depending on the subject matter, Facebook advertisements can anticipate the New York Times. In the contexts of immigration, racism, and the LGBT community, such patterns of content adaptation are more prominent. We use the results to provide policy and research recommendations.

53268 Analysis of Google Search Trends on Topical Steroid Withdrawal Syndrome

53268 Analysis of Google Search Trends on Topical Steroid Withdrawal Syndrome

How Credible Is the Central Bank of Russia to Russian Households?

The article examines the degree of public trust in the Central Bank of Russia after the transition to inflation targeting. For this purpose, the author analyzes available data from inFOM consumer surveys of confidence in the Bank of Russia as a whole as well as in its inflation targets from 2014 to 2024. Public confidence in the Bank of Russia remained relatively high from 2014 to 2017 as inflation targeting was adopted, but the Bank became far less credible to households from 2019 to 2022 because they did not believe the inflation target of 4% was achievable by the end of the current year or over the medium term. This is probably due to their lack of awareness that the Bank’s principal goal is price stability even though they are actually experiencing high inflation. The households surveyed think that government authorities are largely responsible for controlling inflation. Furthermore, consumers do not take notice of the information provided by the Central Bank of Russia. Using news articles from Russian media, posts from news communities on the VKontakte social network, and Google Trends search queries, the author shows that consumers who ignore information about the Bank’s policy are at a serious disadvantage when there is economic uncertainty. The discrepancy between the inflation expectations of consumers and professional forecasters diminishes as the volume of information tied to the phrase “Bank of Russia” increases in media, social network posts, and online searches.

Drought Awareness over Continental United States

Understanding the relationship between droughts and drought awareness is vital towards decision making and policy for water management and conservation strategies, and socioeconomic outcomes. We used computer vision (UNet models) to analyze nonlinear, heterogeneous, lagged correlations between Standardized Precipitation Evapotranspiration Index (SPEI) and Google Trends Search Interest within the Continental United States (CONUS). The most important drivers of the relationship between drought occurrence and drought awareness are the variability and ranges of drought trends and severity, as well as extreme drought conditions. This relationship was the strongest for Western states, followed by Northeastern, Southeastern, and Central regions. Search interest tends to lag droughts by a period of̃1-3 months. We also found evidence that reductionist linear approaches, such as a Principal Component Analysis, might not be as effective as UNet models in capturing the nuanced relationship between droughts and drought awareness at various dimensions and scales.

Characterizing the Online Discourse on Facial Paralysis: What Patients Are Asking and Where They Find Answers.

Background: With the rising popularity of online search tools, patients seeking information on facial palsy are increasingly turning to the Internet for medical knowledge. Objective: To categorize the most common online questions about Bell's palsy or facial paralysis and the sources that provide answers to those queries. Methods: Query volumes for terms pertaining to facial palsy were obtained using Google Search trends. The top 40 keywords associated with the terms "Bell's palsy" and "facial paralysis" were extracted. People Also Ask (PAA) Questions-a Google search engine response page feature-were used to identify the top questions associated with each keyword. Results: A total of 151 PAA Questions pertaining to the top 40 keywords associated with "Bell's palsy" and "facial paralysis" were identified. Etiology questions were most frequent (n = 50, 33.1%), meanwhile those pertaining to treatment were most accessible (119.5 average search engine response pages/question, 35.5%). Most sources were academic (n = 81, 53.6%). Medical practice group sites were most accessible (211.9 average search engine response pages/website, 44.8%). Conclusion: Most PAA questions pertained to etiology and were sourced by academic sites. Questions regarding treatment and medical practice sites appeared on more search engine response pages when compared with all other categories.

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.

Investors’ attention and network spillover for commodity market forecasting

This paper explores the role of network spillovers in commodity market forecasting and proposes a novel factor-augmented dynamic network model. We focus on a novel network definition based on investors’ attention to commodities, positing that commodities exhibit spillovers if they share a similar level of interest. To this aim, we employ Google Trends search data as an instrumental measure for attention. The results reveal that including attention-driven spillovers significantly enhances the forecasting accuracy of commodity returns.

Mental Health Applications of Generative AI and Large Language Modeling in the United States.

(1) Background: Artificial intelligence (AI) has flourished in recent years. More specifically, generative AI has had broad applications in many disciplines. While mental illness is on the rise, AI has proven valuable in aiding the diagnosis and treatment of mental disorders. However, there is little to no research about precisely how much interest there is in AI technology. (2) Methods: We performed a Google Trends search for "AI and mental health" and compared relative search volume (RSV) indices of "AI", "AI and Depression", and "AI and anxiety". This time series study employed Box-Jenkins time series modeling to forecast long-term interest through the end of 2024. (3) Results: Within the United States, AI interest steadily increased throughout 2023, with some anomalies due to media reporting. Through predictive models, we found that this trend is predicted to increase 114% through the end of the year 2024, with public interest in AI applications being on the rise. (4) Conclusions: According to our study, we found that the awareness of AI has drastically increased throughout 2023, especially in mental health. This demonstrates increasing public awareness of mental health and AI, making advocacy and education about AI technology of paramount importance.

Analyzing Google Search Trends for Migraine Surgery and Nurtec in Response to Public Announcements.

Nurtec, a versatile migraine medication, has gained popularity. However, the awareness of migraine surgery remains uncertain. Following a descriptive approach, this cross-sectional study used Google Trends data as of December 1, 2023, to analyze internet search patterns. Approval from Vanderbilt University's institutional review board and adherence to Strengthening the Reporting of Observational Studies in Epidemiology guidelines were confirmed. Monthly relative search volume (RSV) data for "migraine surgery," "Nurtec," and "Rimegepant" were collected from January 1, 2004, to November 11, 2023, within the United States. Statistical analysis involved determining mean monthly RSV values and percentage changes for critical periods. For "Nurtec," a significant surge in RSV occurred from March 2020 to April 2020 (344%). Additional peaks were observed from June 2020 to July 2020 (66%), October 2020 to December 2020 (169%), May 2021 to June 2021 (33%), and May 2023 to June 2023 (14%). "Migraine surgery" exhibited a notable 400% increase in RSV, from March 2005 to May 2005. However, post-2006, RSV for "migraine surgery" consistently remained low without noticeable peaks. The analysis of RSV trends for "Nurtec" and "migraine surgery" from 2004 to 2023 reveals the impact of pivotal events and marketing strategies on public interest. The distinct peaks in "Nurtec" RSV align with Food & Drug Administration approvals and marketing campaigns, highlighting the medication's accessibility. Conversely, the consistently low RSV for "migraine surgery" indicates limited awareness, emphasizing the need for enhanced promotion and education regarding surgical interventions.

ChatGPT can help guide and empower patients after prostate cancer diagnosis.

Patients often face uncertainty about what they should know after prostate cancer diagnosis. Web-based information is common but is at risk of being of poor quality or readability. We used ChatGPT, a freely available Artificial intelligence (AI) platform, to generate enquiries about prostate cancer that a newly diagnosed patient might ask and compared to Google search trends. Then, we evaluated ChatGPT responses to these questions for clinical appropriateness and quality using standardised tools. ChatGPT generates broad and representative questions, and provides understandable, clinically sound advice. AI can guide and empower patients after prostate cancer diagnosis through education. However, the limitations of the ChatGPT language-model must not be ignored and require further evaluation and optimisation in the healthcare field.

Google trends as an early indicator of African swine fever outbreaks in Southeast Asia.

African Swine Fever (ASF) is a reportable disease of swine that causes far-reaching losses to affected countries and regions. Early detection is critically important to contain and mitigate the impact of ASF outbreaks, for which timely available data is essential. This research examines the potential use of Google Trends data as an early indicator of ASF outbreaks in Southeast Asia, focusing on the three largest swine producing countries, namely, Vietnam, the Philippines, and Thailand. Cross-correlation and Kullback-Leibler (KL) divergence indicators were used to evaluate the association between Google search trends and the number of ASF outbreaks reported. Our analysis indicate strong and moderate correlations between Google search trends and number of ASF outbreaks reported in Vietnam and the Philippines, respectively. In contrast, Thailand, the country of this group in which outbreaks were reported last, exhibits the weakest correlation (KL = 2.64), highlighting variations in public awareness and disease dynamics. These findings suggest that Google search trends are valuable for early detection of ASF. As the disease becomes endemic, integrating trends with other epidemiological data may support the design and implementation of surveillance strategies for transboundary animal diseases in Southeast Asia.

"What's in a name?": Using mpox as a case study to understand the importance of communication, advocacy, and information accuracy in disease nomenclature.

Historically, many diseases have been named after the species or location of discovery, the discovering scientists, or the most impacted population. However, species-specific disease names often misrepresent the true reservoir; location-based disease names are frequently targeted with xenophobia; some of the discovering scientists have darker histories; and impacted populations have been stigmatized for this association. Acknowledging these concerns, the World Health Organization now proposes naming diseases after their causative pathogen or symptomatology. Recently, this guidance has been retrospectively applied to a disease at the center of an outbreak rife with stigmatization and misinformation: mpox (f.k.a. 'monkeypox'). This disease, historically endemic to west and central Africa, has prompted racist remarks as it spread globally in 2022 in an epidemic ongoing today. Moreover, its elevated prevalence among men who have sex with men has yielded increased stigma against the LGBTQ+ community. To address these prejudicial associations, 'monkeypox' was renamed 'mpox' in November 2022. We used publicly available data from Google Search Trends to determine which countries were quicker to adopt this name change-and understand factors that limit or facilitate its use. Specifically, we built regression models to quantify the relationship between 'mpox' search intensity in a given country and the country's type of political regime, robustness of sociopolitical and health systems, level of pandemic preparedness, extent of gender and educational inequalities, and temporal evolution of mpox cases through December 2023. Our results suggest that, when compared to 'monkeypox' search intensity, 'mpox' search intensity was significantly higher in countries with any history of mpox outbreaks or higher levels of LGBTQ+ acceptance; meanwhile, 'mpox' search intensity was significantly lower in countries governed by leaders who had recently propagated infectious disease misinformation. Among infectious diseases with stigmatizing names, mpox is among the first to be revised retrospectively. While the adoption of a given disease name will be context-specific-depending in part on its origins and the affected subpopulations-our study provides generalizable insights, applicable to future changes in disease nomenclature.