Online searches to evaluate misinformation can increase its perceived veracity.

Considerable scholarly attention has been paid to understanding belief in online misinformation1,2, with a particular focus on social networks. However, the dominant role of search engines in the information environment remains underexplored, even though the use of online search to evaluate the veracity of information is a central component of media literacy interventions3-5. Although conventional wisdom suggests that searching online when evaluating misinformation would reduce belief in it, there is little empirical evidence to evaluate this claim. Here, across five experiments, we present consistent evidence that online search to evaluate the truthfulness of false news articles actually increases the probability of believing them. To shed light on this relationship, we combine survey data with digital trace data collected using a custom browser extension. We find that the search effect is concentrated among individuals for whom search engines return lower-quality information. Our results indicate that those who search online to evaluate misinformation risk falling into data voids, or informational spaces in which there is corroborating evidence from low-quality sources. We also find consistent evidence that searching online to evaluate news increases belief in true news from low-quality sources, but inconsistent evidence that it increases belief in true news from mainstream sources. Our findings highlight the need for media literacy programmes to ground their recommendations in empirically tested strategies and for search engines to invest in solutions to the challenges identified here.

Providing early indication of regional anomalies in COVID-19 case counts in England using search engine queries.

Prior work has shown the utility of using Internet searches to track the incidence of different respiratory illnesses. Similarly, people who suffer from COVID-19 may query for their symptoms prior to accessing the medical system (or in lieu of it). To assist in the UK government's response to the COVID-19 pandemic we analyzed searches for relevant symptoms on the Bing web search engine from users in England to identify areas of the country where unexpected rises in relevant symptom searches occurred. These were reported weekly to the UK Health Security Agency to assist in their monitoring of the pandemic. Our analysis shows that searches for "fever" and "cough" were the most correlated with future case counts during the initial stages of the pandemic, with searches preceding case counts by up to 21 days. Unexpected rises in search patterns were predictive of anomalous rises in future case counts within a week, reaching an Area Under Curve of 0.82 during the initial phase of the pandemic, and later reducing due to changes in symptom presentation. Thus, analysis of regional searches for symptoms can provide an early indicator (of more than one week) of increases in COVID-19 case counts.

Associations between COVID-19 mobility restrictions and economic, mental health, and suicide-related concerns in the US using cellular phone GPS and Google search volume data.

During the COVID-19 pandemic, US populations have experienced elevated rates of financial and psychological distress that could lead to increases in suicide rates. Rapid ongoing mental health monitoring is critical for early intervention, especially in regions most affected by the pandemic, yet traditional surveillance data are available only after long lags. Novel information on real-time population isolation and concerns stemming from the pandemic's social and economic impacts, via cellular mobility tracking and online search data, are potentially important interim surveillance resources. Using these measures, we employed transfer function model time-series analyses to estimate associations between daily mobility indicators (proportion of cellular devices completely at home and time spent at home) and Google Health Trends search volumes for terms pertaining to economic stress, mental health, and suicide during 2020 and 2021 both nationally and in New York City. During the first pandemic wave in early-spring 2020, over 50% of devices remained completely at home and searches for economic stressors exceeded 60,000 per 10 million. We found large concurrent associations across analyses between declining mobility and increasing searches for economic stressor terms (national proportion of devices at home: cross-correlation coefficient (CC) = 0.6 (p-value <0.001)). Nationally, we also found strong associations between declining mobility and increasing mental health and suicide-related searches (time at home: mood/anxiety CC = 0.53 (<0.001), social stressor CC = 0.51 (<0.001), suicide seeking CC = 0.37 (0.006)). Our findings suggest that pandemic-related isolation coincided with acute economic distress and may be a risk factor for poor mental health and suicidal behavior. These emergent relationships warrant ongoing attention and causal assessment given the potential for long-term psychological impact and suicide death. As US populations continue to face stress, Google search data can be used to identify possible warning signs from real-time changes in distributions of population thought patterns.

A Google Trends™ Analysis of Bladder Cancer: Determining Awareness Campaign Success, and Patients' Needs in Clinical Management.

OBJECTIVE: We aimed to determine the interest and changing trends over time in the diagnosis and treatment of bladder cancer and its awareness campaign by examining the Google Trends application as an indicator of people's interest globally. METHODS: Using the Google Trends application, we determined the yearly and country-based relative search volumes of the term "bladder tumor" and of the methods used in the diagnosis and treatment of bladder cancer in the period from January 2004 to December 2019. We compared the median relative search volumes found in the period 2004-2011 (Period 1) with those found in the period 2012-2019 (Period 2). RESULTS: We found that the median relative search volume for bladder cancer decreased in period 2 and this was parallel to the decrease in the incidence rates in North America and Australia (p<0.001). We found that the bladder cancer awareness month did not cause an increase in the online interest (p>0.05). We found that the median relative search volumes of diagnostic cystoscopy and cytology were higher than those of molecular markers and imaging methods in line with guidelines (p<0.001). Also, TURBT was the most sought-term among treatment methods with increasing popularity in the second period (p<0.001). CONCLUSION: People use the internet intensively to search for information about bladder cancer. We think that several types of web-based applications such as "Google Trends" can help determine the behavioural patterns and tendencies of bladder cancer patients and affect the clinical decision-making processes, as well as readily determining the impact of cancer awareness campaigns to bring about an increased awareness in the society for the recognition of the importance of an early diagnosis.

The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis.

This study aims to evaluate the monitoring and predictive value of web-based symptoms (fever, cough, dyspnea) searches for COVID-19 spread. Daily search interests from Turkey, Italy, Spain, France, and the United Kingdom were obtained from Google Trends (GT) between January 1, 2020, and August 31, 2020. In addition to conventional correlational models, we studied the time-varying correlation between GT search and new case reports; we used dynamic conditional correlation (DCC) and sliding windows correlation models. We found time-varying correlations between pulmonary symptoms on GT and new cases to be significant. The DCC model proved more powerful than the sliding windows correlation model. This model also provided better at time-varying correlations (r ≥ 0.90) during the first wave of the pandemic. We used a root means square error (RMSE) approach to attain symptom-specific shift days and showed that pulmonary symptom searches on GT should be shifted separately. Web-based search interest for pulmonary symptoms of COVID-19 is a reliable predictor of later reported cases for the first wave of the COVID-19 pandemic. Illness-specific symptom search interest on GT can be used to alert the healthcare system to prepare and allocate resources needed ahead of time.

Decreased Global Interest in Oral Cancer During the COVID-19 Pandemic.

OBJECTIVE: Oral cancer is one of the most common malignancies in developing countries, but studies using global data are scarce. The aim of this study is to analyze the search interests for oral cancer using mouth cancer, tongue cancer, gum cancer, and lip cancer as common keywords. METHODS: Internet searches relating to oral cancer from 2010 to 2020, from 250 countries and dependent areas, were retrieved from Google Trends. Color densities in a heat map were used to show geographic differences. Kruskal-Wallis test with post hoc Dunn's analysis was used to perform yearly comparisons of searches for mouth cancer, tongue cancer, gum cancer, and lip cancer. Search results within 2020 were also compared to determine differences. Forecasting searches from 2021 to 2022 were done by fitting time series models. RESULTS: From 29 of 250 (11.6%) countries, the highest search values were observed for mouth cancer in Sri Lanka, Qatar, Bangladesh, Finland, Netherlands, Spain, and France. Compared to 2020, greater searches were seen in 2018 (Mdn = 91%, P = 0.023) and 2019 (Mdn = 94%, P = 0.012) for mouth cancer, and 2019 (Mdn = 17%, P = 0.035) for lip cancer. The relative search volumes for gum cancer and lip cancer were substantially lower than mouth cancer during the COVID-19 pandemic. CONCLUSION: Higher-income countries tend to be more interested in seeking information about oral cancer. Noteworthy decline in the interest in seeking information online for oral cancer may have crucial implications during the COVID-19 pandemic. Google Trends offer an invaluable and inexpensive means for oral cancer surveillance and health-seeking behavior. 
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Association of Rideshare Use With Alcohol-Associated Motor Vehicle Crash Trauma.

Importance: Motor vehicle crashes (MVCs) are an important public health concern. Recent trends suggest that introducing rideshare services has decreased the incidence of MVCs. However, detailed analyses linking rideshare volume, convictions for impaired driving, and nonfatal MVC traumas remain inconclusive. Objective: To determine if there is an association between rideshare use and MVC traumas and convictions for impaired driving in Houston, Texas. Design, Setting, and Participants: This multicenter cohort study was conducted between January 2007 and November 2019 with hospital data from the Red Duke Trauma Institute within the Memorial Hermann Hospital-Texas Medical Center and Ben Taub General Hospital. Rideshare data from Uber and Google covered trips taken within Houston, Texas, from February 2014 (the date of deployment of Uber to Houston) to December 2018. Impaired driving convictions included all indictments made by the Harris County, Texas, District Attorney's office from January 2007 to December 2018. All adults with MVC traumas evaluated at both centers in the study population (individuals >16 years with a mechanism of injury classified under "motor vehicle collision") were included. Impaired driving incidents were included only if the final legal outcome was conviction. Main Outcomes and Measures: The primary study outcomes were the incident rate ratios for hourly MVC traumas and daily impaired driving convictions. Results: A total of 23 491 MVC traumas (involving patients with a mean [SD] age of 37.9 [17.8] years and 14 603 male individuals [62.1%]), 93 742 impaired driving convictions, and more than 24 million Uber rides were analyzed. Following the introduction of Uber in February 2014, MVC traumas decreased by 23.8% (from a mean [SD] of 0.26 [0.04] to 0.21 [0.06] trauma incidents per hour) during peak trauma periods (Friday and Saturday nights). The incident rate ratio of MVC traumas following Uber deployment was 0.33 (95% CI, 0.17-0.67) per 1000 indexed rides (P = .002). Furthermore, rideshare use was associated with a significant, geographically linked reduction in impaired driving convictions between January 2014 to December 2019 (incidence rate ratio, 0.76 [95% CI, 0.73-0.78]; P < .001). Conclusions and Relevance: In this study, introducing rideshare services in the Houston metropolitan area was associated with significant reductions in MVC traumas and impaired driving convictions. Increased use of rideshares may be an effective means of reducing impaired driving and decreasing rate of MVC traumas.

Measuring Public Concern About COVID-19 in Japanese Internet Users Through Search Queries: Infodemiological Study.

BACKGROUND: COVID-19 has disrupted lives and livelihoods and caused widespread panic worldwide. Emerging reports suggest that people living in rural areas in some countries are more susceptible to COVID-19. However, there is a lack of quantitative evidence that can shed light on whether residents of rural areas are more concerned about COVID-19 than residents of urban areas. OBJECTIVE: This infodemiology study investigated attitudes toward COVID-19 in different Japanese prefectures by aggregating and analyzing Yahoo! JAPAN search queries. METHODS: We measured COVID-19 concerns in each Japanese prefecture by aggregating search counts of COVID-19-related queries of Yahoo! JAPAN users and data related to COVID-19 cases. We then defined two indices-the localized concern index (LCI) and localized concern index by patient percentage (LCIPP)-to quantitatively represent the degree of concern. To investigate the impact of emergency declarations on people's concerns, we divided our study period into three phases according to the timing of the state of emergency in Japan: before, during, and after. In addition, we evaluated the relationship between the LCI and LCIPP in different prefectures by correlating them with prefecture-level indicators of urbanization. RESULTS: Our results demonstrated that the concerns about COVID-19 in the prefectures changed in accordance with the declaration of the state of emergency. The correlation analyses also indicated that the differentiated types of public concern measured by the LCI and LCIPP reflect the prefectures' level of urbanization to a certain extent (ie, the LCI appears to be more suitable for quantifying COVID-19 concern in urban areas, while the LCIPP seems to be more appropriate for rural areas). CONCLUSIONS: We quantitatively defined Japanese Yahoo users' concerns about COVID-19 by using the search counts of COVID-19-related search queries. Our results also showed that the LCI and LCIPP have external validity.

Public Interest in Immunity and the Justification for Intervention in the Early Stages of the COVID-19 Pandemic: Analysis of Google Trends Data.

BACKGROUND: The use of social big data is an important emerging concern in public health. Internet search volumes are useful data that can sensitively detect trends of the public's attention during a pandemic outbreak situation. OBJECTIVE: Our study aimed to analyze the public's interest in COVID-19 proliferation, identify the correlation between the proliferation of COVID-19 and interest in immunity and products that have been reported to confer an enhancement of immunity, and suggest measures for interventions that should be implemented from a health and medical point of view. METHODS: To assess the level of public interest in infectious diseases during the initial days of the COVID-19 outbreak, we extracted Google search data from January 20, 2020, onward and compared them to data from March 15, 2020, which was approximately 2 months after the COVID-19 outbreak began. In order to determine whether the public became interested in the immune system, we selected coronavirus, immune, and vitamin as our final search terms. RESULTS: The increase in the cumulative number of confirmed COVID-19 cases that occurred after January 20, 2020, had a strong positive correlation with the search volumes for the terms coronavirus (R=0.786; P<.001), immune (R=0.745; P<.001), and vitamin (R=0.778; P<.001), and the correlations between variables were all mutually statistically significant. Moreover, these correlations were confirmed on a country basis when we restricted our analyses to the United States, the United Kingdom, Italy, and Korea. Our findings revealed that increases in search volumes for the terms coronavirus and immune preceded the actual occurrences of confirmed cases. CONCLUSIONS: Our study shows that during the initial phase of the COVID-19 crisis, the public's desire and actions of strengthening their own immune systems were enhanced. Further, in the early stage of a pandemic, social media platforms have a high potential for informing the public about potentially helpful measures to prevent the spread of an infectious disease and provide relevant information about immunity, thereby increasing the public's knowledge.

Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study.

BACKGROUND: The prevalence of chronic conditions such as obesity, hypertension, and diabetes is increasing in African countries. Many chronic diseases have been linked to risk factors such as poor diet and physical inactivity. Data for these behavioral risk factors are usually obtained from surveys, which can be delayed by years. Behavioral data from digital sources, including social media and search engines, could be used for timely monitoring of behavioral risk factors. OBJECTIVE: The objective of our study was to propose the use of digital data from internet sources for monitoring changes in behavioral risk factors in Africa. METHODS: We obtained the adjusted volume of search queries submitted to Google for 108 terms related to diet, exercise, and disease from 2010 to 2016. We also obtained the obesity and overweight prevalence for 52 African countries from the World Health Organization (WHO) for the same period. Machine learning algorithms (ie, random forest, support vector machine, Bayes generalized linear model, gradient boosting, and an ensemble of the individual methods) were used to identify search terms and patterns that correlate with changes in obesity and overweight prevalence across Africa. Out-of-sample predictions were used to assess and validate the model performance. RESULTS: The study included 52 African countries. In 2016, the WHO reported an overweight prevalence ranging from 20.9% (95% credible interval [CI] 17.1%-25.0%) to 66.8% (95% CI 62.4%-71.0%) and an obesity prevalence ranging from 4.5% (95% CI 2.9%-6.5%) to 32.5% (95% CI 27.2%-38.1%) in Africa. The highest obesity and overweight prevalence were noted in the northern and southern regions. Google searches for diet-, exercise-, and obesity-related terms explained 97.3% (root-mean-square error [RMSE] 1.15) of the variation in obesity prevalence across all 52 countries. Similarly, the search data explained 96.6% (RMSE 2.26) of the variation in the overweight prevalence. The search terms yoga, exercise, and gym were most correlated with changes in obesity and overweight prevalence in countries with the highest prevalence. CONCLUSIONS: Information-seeking patterns for diet- and exercise-related terms could indicate changes in attitudes toward and engagement in risk factors or healthy behaviors. These trends could capture population changes in risk factor prevalence, inform digital and physical interventions, and supplement official data from surveys.

Google Trends for Pain Search Terms in the World's Most Populated Regions Before and After the First Recorded COVID-19 Case: Infodemiological Study.

BACKGROUND: Web-based analysis of search queries has become a very useful method in various academic fields for understanding timely and regional differences in the public interest in certain terms and concepts. Particularly in health and medical research, Google Trends has been increasingly used over the last decade. OBJECTIVE: This study aimed to assess the search activity of pain-related parameters on Google Trends from among the most populated regions worldwide over a 3-year period from before the report of the first confirmed COVID-19 cases in these regions (January 2018) until December 2020. METHODS: Search terms from the following regions were used for the analysis: India, China, Europe, the United States, Brazil, Pakistan, and Indonesia. In total, 24 expressions of pain location were assessed. Search terms were extracted using the local language of the respective country. Python scripts were used for data mining. All statistical calculations were performed through exploratory data analysis and nonparametric Mann-Whitney U tests. RESULTS: Although the overall search activity for pain-related terms increased, apart from pain entities such as headache, chest pain, and sore throat, we observed discordant search activity. Among the most populous regions, pain-related search parameters for shoulder, abdominal, and chest pain, headache, and toothache differed significantly before and after the first officially confirmed COVID-19 cases (for all, P<.001). In addition, we observed a heterogenous, marked increase or reduction in pain-related search parameters among the most populated regions. CONCLUSIONS: As internet searches are a surrogate for public interest, we assume that our data are indicative of an increased incidence of pain after the onset of the COVID-19 pandemic. However, as these increased incidences vary across geographical and anatomical locations, our findings could potentially facilitate the development of specific strategies to support the most affected groups.

Using Real-Time Google Search Interest as a Predictive Tool for COVID-19 Cases in the United States.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is an ongoing global pandemic with over 23 million associated cases and 800,000 associated deaths. There is a surplus of proposed predictive models (n>145) for COVID-19 that have emerged in academic literature; however, many of these predictive models have proven unreliable or biased.1 Several studies have looked at Google Trends data as a possible predictive tool in the last months.2-12 In this retrospective study, we looked at the predictive value of the Google Trends Tool as it applies to COVID-19 Cases and Reported Onset of Symptoms in the US. We looked back at Google Trends data for search interest of common COVID-19 search terms: "coronavirus" and "covid-19" from January 2020 through mid-June 2020 and compared that data to Centers for Disease Control (CDC) data on COVID-19 Cases Reported and Reported Date of Initial Onset of Symptoms.13 Google Trends is a free online tool that allows a user to quantify the search interest for a keyword or phrase over time.14 Significant strong positive correlation was found between CDC Reported Date of Initial Symptoms for Cases data and Search Interest for both terms "covid-19" and "coronavirus." Google Trends is a free and easy to access tool that may have utility as a predictive instrument with regards to the current COVID-19 pandemic. The Google Trends Tool may offer new insight and predictive value for medical decision making during current and future outbreaks in near-real time at a very granular level allowing states, cities and military bases to prepare.

Usefulness of web search queries for early detection of diseases in infants.

Early detection of diseases is critical in infants. This study evaluates the usefulness of web searches in predicting diseases in order to encourage guardians to consult a doctor promptly if their children are ill. We collected six months of search queries from Yahoo! JAPAN Search between October 2016 and March 2017. Using a machine learning model, we investigated the accuracy of the search query's ability to predict the diagnosis of biliary atresia and hypertrophic pyloric stenosis. Both diseases were modeled with an accuracy of approximately 80%, and symptoms related to the disease were significant features in the model. These findings suggest the possibility of detecting diseases from web search queries performed by guardians. Through future research, we intend to propose a method that uses web search queries for early detection of these diseases by providing appropriate and timely information to support the guardians of patients.

Clinical Predictors of SARS-CoV-2 Testing Pressure on Clinical Laboratories: A Multinational Study Analyzing Google Trends and Over 100 Million Diagnostic Tests.

OBJECTIVE: Evidence has shown that Google searches for clinical symptom keywords correlates with the number of new weekly patients with COVID-19. This multinational study assessed whether demand for SARS-CoV-2 tests could also be predicted by Google searches for key COVID-19 symptoms. METHODS: The weekly number of SARS-CoV-2 tests performed in Italy and the United States was retrieved from official sources. A concomitant electronic search was performed in Google Trends, using terms for key COVID-19 symptoms. RESULTS: The model that provided the highest coefficient of determination for the United States (R2 = 82.8%) included a combination of searching for cough (with a time lag of 2 weeks), fever (with a time lag of 2 weeks), and headache (with a time lag of 3 weeks; the time lag refers to the amount of time between when a search was conducted and when a test was administered). In Italy, headache provided the model with the highest adjusted R2 (86.8%), with time lags of both 1 and 2 weeks. CONCLUSION: Weekly monitoring of Google Trends scores for nonspecific COVID-19 symptoms is a reliable approach for anticipating SARS-CoV-2 testing demands ~2 weeks in the future.

A predictive internet-based model for COVID-19 hospitalization census.

The COVID-19 pandemic has strained hospital resources and necessitated the need for predictive models to forecast patient care demands in order to allow for adequate staffing and resource allocation. Recently, other studies have looked at associations between Google Trends data and the number of COVID-19 cases. Expanding on this approach, we propose a vector error correction model (VECM) for the number of COVID-19 patients in a healthcare system (Census) that incorporates Google search term activity and healthcare chatbot scores. The VECM provided a good fit to Census and very good forecasting performance as assessed by hypothesis tests and mean absolute percentage prediction error. Although our study and model have limitations, we have conducted a broad and insightful search for candidate Internet variables and employed rigorous statistical methods. We have demonstrated the VECM can potentially be a valuable component to a COVID-19 surveillance program in a healthcare system.