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This study uses data from Google Trends and Twitter to analyze how public interest and sentiment towards Universal Basic Income (UBI) changed across all 50 states and Washington D.C. between 2018 and 2021. We specifically selected this time period as it includes both Andrew Yang's UBI campaign during the Democratic primaries in 2019 and the COVID-19 pandemic in 2020 when UBI gained attention due to the federal government's unconditional cash payment to almost all citizens. To overcome the limitations of sporadic opinion polls, we built on a recent development of the rescaling method to generate longitudinal Google Trends and conducted Twitter sentiment analysis. We observed a modest rise in public interest in UBI during Andrew Yang's campaign, especially in blue states, and a significant increase across all states at the onset of the COVID-19 pandemic. Although it quickly waned, the level of public attention became elevated compared to the pre-pandemic level. Contrary to previous studies, our analysis also reveals that overall sentiment became less positive after the peak interest during the pandemic, as more people engaged in online discussions. [ABSTRACT FROM AUTHOR]

Abstract Google Trends (GT) collate the volumes of search keywords over time and by geographical location. Such data could, in theory, provide insights into people’s ex ante intentions to migrate, and hence be useful for predictive analysis of future migration. Empirically, however, the predictive power of GT is sensitive, it may vary depending on geographical context, the search keywords selected for analysis, as well as Google’s market share and its users’ characteristics and search behavior, among others. Unlike most previous studies attempting to demonstrate the benefit of using GT for forecasting migration flows, this article addresses a critical but less discussed issue: when GT cannot enhance the performances of migration models. Using EUROSTAT statistics on first-time asylum applications and a set of push-pull indicators gathered from various data sources, we train three classes of gravity models that are commonly used in the migration literature, and examine how the inclusion of GT may affect models’ abilities to predict refugees’ destination choices. The results suggest that the effects of including GT are highly contingent on the complexity of different models. Specifically, GT can only improve the performance of relatively simple models, but not of those augmented by flow Fixed-Effects or by Auto-Regressive effects. These findings call for a more comprehensive analysis of the strengths and limitations of using GT, as well as other digital trace data, in the context of modeling and forecasting migration. It is our hope that this nuanced perspective can spur further innovations in the field, and ultimately bring us closer to a comprehensive modeling framework of human migration.

Google Trends (GT) collate the volumes of search keywords over time and by geographical location. Such data could, in theory, provide insights into people's ex ante intentions to migrate, and hence be useful for predictive analysis of future migration. Empirically, however, the predictive power of GT is sensitive, it may vary depending on geographical context, the search keywords selected for analysis, as well as Google's market share and its users' characteristics and search behavior, among others. Unlike most previous studies attempting to demonstrate the benefit of using GT for forecasting migration flows, this article addresses a critical but less discussed issue: when GT cannot enhance the performances of migration models. Using EUROSTAT statistics on first-time asylum applications and a set of push-pull indicators gathered from various data sources, we train three classes of gravity models that are commonly used in the migration literature, and examine how the inclusion of GT may affect models' abilities to predict refugees' destination choices. The results suggest that the effects of including GT are highly contingent on the complexity of different models. Specifically, GT can only improve the performance of relatively simple models, but not of those augmented by flow Fixed-Effects or by Auto-Regressive effects. These findings call for a more comprehensive analysis of the strengths and limitations of using GT, as well as other digital trace data, in the context of modeling and forecasting migration. It is our hope that this nuanced perspective can spur further innovations in the field, and ultimately bring us closer to a comprehensive modeling framework of human migration. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has led to tremendous disruptions in people’s everyday activities, including the pursuit of education. Internet search data may provide insights into potential audiences’ interest in online education. Using Internet search data, we examined the impact of COVID-19 on people’s interest in supplementary online education in the US over nine months (10/14/2019–07/19/2020). We found there was increased interest in supplementary online education after WHO announced COVID-19 as a pandemic, with a greater increase among females than males. We found that the increased interest in online education persisted after the stay-at-home orders were lifted; in addition, we identified concerns over unemployment as a key variable that significantly explained the variance in the interest in online education, even after controlling for COVID cases and deaths. Policymakers and online education platforms may take advantage of people’s, especially women’s increased interest in online education when designing policies or marketing mix.

BackgroundFor a number of fiscal and practical reasons, data on heroin use have been of poor quality, which has hampered the ability to halt the growing epidemic. Internet search data, such as those made available by Google Trends, have been used as a low-cost, real-time data source for monitoring and predicting a variety of public health outcomes. We aimed to determine whether data on opioid-related internet searches might predict future heroin-related admissions to emergency departments (ED).MethodsAcross nine metropolitan statistical areas (MSAs) in the United States, we obtained data on Google searches for prescription and non-prescription opioids, as well as Substance Abuse and Mental Health Services Administration (SAMHSA) data on heroin-related ED visits from 2004 to 2011. A linear mixed model assessed the relationship between opioid-related Internet searches and following year heroin-related visits, controlling for MSA GINI index and total number of ED visits.ResultsThe best-fitting model explained 72% of the variance in heroin-related ED visits. The final model included the search keywords "Avinza," "Brown Sugar," "China White," "Codeine," "Kadian," "Methadone," and "Oxymorphone." We found regional differences in where and how people searched for opioid-related information.ConclusionsInternet search-based modeling should be explored as a new source of insights for predicting heroin-related admissions. In geographic regions where no current heroin-related data exist, Internet search modeling might be a particularly valuable and inexpensive tool for estimating changing heroin use trends. We discuss the immediate implications for using this approach to assist in managing opioid-related morbidity and mortality in the United States.

With the rapid development of the internet, the application of internet search data has been seen as a novel data source to offer timely infectious disease surveillance intelligence. Moreover, the advancements in internet search data, which include rich information at both space and time scales, enable investigators to sufficiently consider the spatiotemporal uncertainty, which can benefit researchers to better monitor infectious diseases and epidemics. In the present study, we present the necessary groundwork and critical appraisal of the use of internet search data and spatiotemporal analysis approaches in infectious disease surveillance by updating the current stage of knowledge on them. The study also provides future directions for researchers to investigate the combination of internet search data with the spatiotemporal analysis in infectious disease surveillance. Internet search data demonstrate a promising potential to offer timely epidemic intelligence, which can be seen as the prerequisite for improving infectious disease surveillance.

Copyright is under contest in Australia amid growing digital cultures of sharing. Using metaphor as a frame for analysis, this study applies internet search data (Google Trends) methods to visualise Australian online information-seeking patterns for metaphors related to copyright and sharing. An overview of legal metaphors of online copyright (‘piracy’, ‘war on copyright’) and metaphors of digital sharing (‘sharing is caring’, ‘sharing economy’) leads to a critical examination of the ‘metaphor struggles’ between the rhetoric of copyright infringement and sharing cultures promoted by social media. Key findings presented are of decreased information seeking for copyright metaphors and increased information seeking for sharing metaphors. Online information-seeking patterns, as visualised by internet search data, represent a form of public mobilisation. Visualisation of these patterns of public information seeking for metaphors of copyright and sharing demonstrates shifting conceptions of copyright in contemporary digital cultures. This article concludes by raising a potential relationship between rising ethics of online sharing norms and diminishing legitimacy of online copyright, as the legal metaphor of copyright appears to transition through the metaphor cycle.

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.

Heat stroke is a serious heat‐related health outcome that can eventually lead to death. Due to the poor accessibility of heat stroke data, the large‐scale relationship between heat stroke and meteorological factors is still unclear. This work aims to clarify the potential relationship between meteorological variables and heat stroke, and quantify the meteorological threshold that affected the severity of heat stroke. We collected daily heat stroke search index (HSSI) and meteorological data for the period 2013–2020 in 333 Chinese cities to analyze the relationship between meteorological variables and HSSI using correlation analysis and Random forest (RF) model. Temperature and relative humidity (RH) accounted for 62% and 9% of the changes of HSSI, respectively. In China, cases of heat stroke may start to occur when temperature exceeds 36°C and RH exceeds 58%. This threshold was 34.5°C and 79% in the north of China, and 36°C and 48% in the south of China. Compared to RH, the threshold of temperature showed a more evident difference affected by altitude and distance from the ocean, which was 35.5°C in inland cities and 36.5°C in coastal cities; 35.5°C in high‐altitude cities and 36°C in low‐altitude cities. Our findings provide a possible way to analyze the interaction effect of meteorological variables on heat‐related illnesses, and emphasizes the effects of geographical environment. The meteorological threshold quantified in this research can also support policymaker to establish a better meteorological warning system for public health. Plain Language Summary: The impact of extreme heat events on population has become of urgent public health concern. In China, the real mortality and morbidity data are not publicly available and data of some diseases are not enough recorded. Thus, it is difficult to build a solid relationship between heat strokes and meteorological variables in large spatial scale. Internet search data, highly correlated with real heat stoke cases, was adopted as a new data set in our research instead of the heat‐related morbidity data to investigate the relationship between heat strokes and multiple meteorological variables. According to Random forest model, temperature and relative humidity (RH) were identified as the most important factor, which mainly affected the severity of heat strokes. We quantified the meteorological threshold that affected the severity of heat strokes as well. Heat strokes may start to occur when temperature exceeds 36°C and RH exceeds 58% in China. The temperature of this threshold is higher in the south or low‐latitude region or coastal region of China. This work provides new insights for health research and help to timely alert the public in adverse weather conditions. Key Points: Internet search data could help to evaluate the health impact in large scale to address the data shortageIncreasing temperature and relative humidity (RH) led to sharply increased heat stroke, particularly when maximum temperature exceeds 36°C and RH exceeds 58%Meteorological threshold was affected by geographical factors like altitude, latitude and distance from ocean [ABSTRACT FROM AUTHOR]

Before making travel plans, people often use the Internet to collect relevant information to help themselves make better decisions. Among the numerous information search channels, Internet search engine is used by the vast number of travelers because of its low cost and high efficiency. To a large extent, Internet search behavior is the external manifestation of users' psychological activities, reflecting their concerns, needs and preferences. Therefore, Internet search data can reflect the air passenger demand information to a certain extent. In this manuscript, a novel decomposition ensemble model is proposed to discuss the role of Internet search data in air passenger demand forecasting. In the empirical study, the relevant data of Shanghai Pudong International Airport and Beijing Capital International Airport are taken as samples. The results show that the proposed forecasting model can integrate the advantages of decomposition-ensemble strategy and deep learning algorithm, and achieve more accurate and reliable prediction results than all benchmark models. This further indicates that adding Internet search data into the forecasting model can effectively improve the prediction performance of air passenger demand, and can provide scientific and reliable decision support for air transport management. [ABSTRACT FROM AUTHOR]

To avoid the effects of systemic financial risks caused by extreme fluctuations in housing price, the Chinese government has been exploring the most effective policies for regulating the housing market. Measuring the effect of real estate regulation policies has been a challenge for present studies. This study innovatively employs big data technology to obtain Internet search data (ISD) and construct market concern index (MCI) of policy, and hedonic price theory to construct hedonic price index (HPI) based on building area, age, ring number, and other hedonic variables. Then, the impact of market concerns for restrictive policy, monetary policy, fiscal policy, security policy, and administrative supervision policy on housing prices is evaluated. Moreover, compared with the common housing price index, the hedonic price index considers the heterogeneity of houses and could better reflect the changes in housing prices caused by market supply and demand. The results indicate that (1) a long-term interaction relationship exists between housing prices and market concerns for policy (MCP); (2) market concerns for restrictive policy and administrative supervision policy effectively restrain rising housing prices while those for monetary and fiscal policy have the opposite effect. The results could serve as a useful reference for governments aiming to stabilize their real estate markets. [ABSTRACT FROM AUTHOR]

Abstract Heat stroke is a serious heat‐related health outcome that can eventually lead to death. Due to the poor accessibility of heat stroke data, the large‐scale relationship between heat stroke and meteorological factors is still unclear. This work aims to clarify the potential relationship between meteorological variables and heat stroke, and quantify the meteorological threshold that affected the severity of heat stroke. We collected daily heat stroke search index (HSSI) and meteorological data for the period 2013–2020 in 333 Chinese cities to analyze the relationship between meteorological variables and HSSI using correlation analysis and Random forest (RF) model. Temperature and relative humidity (RH) accounted for 62% and 9% of the changes of HSSI, respectively. In China, cases of heat stroke may start to occur when temperature exceeds 36°C and RH exceeds 58%. This threshold was 34.5°C and 79% in the north of China, and 36°C and 48% in the south of China. Compared to RH, the threshold of temperature showed a more evident difference affected by altitude and distance from the ocean, which was 35.5°C in inland cities and 36.5°C in coastal cities; 35.5°C in high‐altitude cities and 36°C in low‐altitude cities. Our findings provide a possible way to analyze the interaction effect of meteorological variables on heat‐related illnesses, and emphasizes the effects of geographical environment. The meteorological threshold quantified in this research can also support policymaker to establish a better meteorological warning system for public health.

Before making travel plans, people often use the Internet to collect relevant information to help themselves make better decisions. Among the numerous information search channels, Internet search engine is used by the vast number of travelers because of its low cost and high efficiency. To a large extent, Internet search behavior is the external manifestation of users’ psychological activities, reflecting their concerns, needs and preferences. Therefore, Internet search data can reflect the air passenger demand information to a certain extent. In this manuscript, a novel decomposition ensemble model is proposed to discuss the role of Internet search data in air passenger demand forecasting. In the empirical study, the relevant data of Shanghai Pudong International Airport and Beijing Capital International Airport are taken as samples. The results show that the proposed forecasting model can integrate the advantages of decomposition-ensemble strategy and deep learning algorithm, and achieve more accurate and reliable prediction results than all benchmark models. This further indicates that adding Internet search data into the forecasting model can effectively improve the prediction performance of air passenger demand, and can provide scientific and reliable decision support for air transport management.

Copyright is under contest in Australia amid growing digital cultures of sharing. Using metaphor as a frame for analysis, this study applies internet search data (Google Trends) methods to visualise Australian online information-seeking patterns for metaphors related to copyright and sharing. An overview of legal metaphors of online copyright ('piracy', 'war on copyright') and metaphors of digital sharing ('sharing is caring', 'sharing economy') leads to a critical examination of the 'metaphor struggles' between the rhetoric of copyright infringement and sharing cultures promoted by social media. Key findings presented are of decreased information seeking for copyright metaphors and increased information seeking for sharing metaphors. Online information-seeking patterns, as visualised by internet search data, represent a form of public mobilisation. Visualisation of these patterns of public information seeking for metaphors of copyright and sharing demonstrates shifting conceptions of copyright in contemporary digital cultures. This article concludes by raising a potential relationship between rising ethics of online sharing norms and diminishing legitimacy of online copyright, as the legal metaphor of copyright appears to transition through the metaphor cycle. [ABSTRACT FROM AUTHOR]

With the increasing popularity of tourism activities, the forecasting of tourist volume has become an important research issue in the field of tourism management. However, the traditional statistical data cannot reflect the changes in tourism demand in real time. In order to make up for this shortcoming, scholars have found that web search data and big data technologies can provide a new way to forecast tourism demand which can expose user behavioral intentions in real time. Accordingly, this paper tries to make a prediction of the number of China inbound foreign tourists based on Google Trends data, and by applying Random Forest (RF) model to this task, a higher prediction accuracy has been achieved. [ABSTRACT FROM AUTHOR]

In recent years, visitation to U.S. National Parks has been increasing, with the majority of this increase occurring in a subset of parks. As a result, managers in these parks must respond quickly to increasing visitor-related challenges. Improved visitation forecasting would allow managers to more proactively plan for such increases. In this study, we leverage internet search data that is freely available through Google Trends to create a forecasting model. We compare this Google Trends model to a traditional autoregressive forecasting model. Overall, our Google Trends model accurately predicted 97% of the total visitation variation to all parks one year in advance from 2013 to 2017 and outperformed the autoregressive model by all metrics. While our Google Trends model performs better overall, this was not the case for each park unit individually; the accuracy of this model varied significantly from park to park. We hypothesized that park attributes related to trip planning would correlate with the accuracy of our Google Trends model, but none of the variables tested produced overly compelling results. Future research can continue exploring the utility of Google Trends to forecast visitor use in protected areas, or use methods demonstrated in this paper to explore alternative data sources to improve visitation forecasting in U.S. National Parks. • Search engine data is useful for predicting visitation to park units the next year. • Overall, using Google Trends performs better than an autoregressive model. • Using Google Trends to forecast visitation is useful for some, but not all, parks. • Utility of Google Trends is weakly correlated with higher surrounding populations. [ABSTRACT FROM AUTHOR]

Background: For a number of fiscal and practical reasons, data on heroin use have been of poor quality, which has hampered the ability to halt the growing epidemic. Internet search data, such as those made available by Google Trends, have been used as a low-cost, real-time data source for monitoring and predicting a variety of public health outcomes. We aimed to determine whether data on opioid-related internet searches might predict future heroin-related admissions to emergency departments (ED).Methods: Across nine metropolitan statistical areas (MSAs) in the United States, we obtained data on Google searches for prescription and non-prescription opioids, as well as Substance Abuse and Mental Health Services Administration (SAMHSA) data on heroin-related ED visits from 2004 to 2011. A linear mixed model assessed the relationship between opioid-related Internet searches and following year heroin-related visits, controlling for MSA GINI index and total number of ED visits.Results: The best-fitting model explained 72% of the variance in heroin-related ED visits. The final model included the search keywords "Avinza," "Brown Sugar," "China White," "Codeine," "Kadian," "Methadone," and "Oxymorphone." We found regional differences in where and how people searched for opioid-related information.Conclusions: Internet search-based modeling should be explored as a new source of insights for predicting heroin-related admissions. In geographic regions where no current heroin-related data exist, Internet search modeling might be a particularly valuable and inexpensive tool for estimating changing heroin use trends. We discuss the immediate implications for using this approach to assist in managing opioid-related morbidity and mortality in the United States. [ABSTRACT FROM AUTHOR]

New data and modeling approaches are improving the usefulness of Internet search data for forecasting inbound tourist arrivals. Previous research has focused on Google Trends as a source of search data to augment tourism forecasting capabilities. In the context of rapidly increasing Chinese outbound tourism Google data lacks the market penetration in China to produce reliable auxiliary data for tourism forecasting. This short paper provides evidence of the usefulness of Baidu search data in predicting Chinese inbound tourist arrivals into a specific region in New Zealand. It also compares three modeling approaches, finding a Vector Autoregressive approach the most useful. [ABSTRACT FROM AUTHOR]

We utilize the Internet search data from Google Trends to provide short-term forecasts for the inflow of Japanese tourists to South Korea. We construct the Google variable in a systematic way by combining keywords to minimize mean squared or mean absolute forecasting errors. We augment the Google variable to the standard time-series forecasting models and compare their forecasting accuracies. We find that Google-augmented models perform much better than the standard time-series models in terms of short-term forecasting accuracy. In particular, Google models show better out-of-sample forecasting performance than in-sample forecasting. [ABSTRACT FROM PUBLISHER]

We use Google searches of the word ‘mortgage’ to explain monthly housing transactions in the Netherlands in the period from 2004 until 2015. Our estimates indicate that Google searches of the previous months are significantly positively associated with housing transactions in the current month. This shows evidence that Internet search data can provide information about real market behaviour. [ABSTRACT FROM AUTHOR]

Background: Anxiety disorders are the most prevalent mental disorders globally, with a substantial impact on quality of life. The prevalence of anxiety disorders has increased substantially following the COVID-19 pandemic, and it is likely to be further affected by a global economic recession. Understanding anxiety themes and how they change over time and across countries is crucial for preventive and treatment strategies., Objective: The aim of this study was to track the trends in anxiety themes between 2004 and 2020 in the 50 most populous countries with high volumes of internet search data. This study extends previous research by using a novel search-based methodology and including a longer time span and more countries at different income levels., Methods: We used a crowdsourced questionnaire, alongside Bing search query data and Google Trends search volume data, to identify themes associated with anxiety disorders across 50 countries from 2004 to 2020. We analyzed themes and their mutual interactions and investigated the associations between countries' socioeconomic attributes and anxiety themes using time-series linear models. This study was approved by the Microsoft Research Institutional Review Board., Results: Query volume for anxiety themes was highly stable in countries from 2004 to 2019 (Spearman r=0.89) and moderately correlated with geography (r=0.49 in 2019). Anxiety themes were predominantly long-term and personal, with "having kids," "pregnancy," and "job" the most voluminous themes in most countries and years. In 2020, "COVID-19" became a dominant theme in 27 countries. Countries with a constant volume of anxiety themes over time had lower fragile state indexes (P=.007) and higher individualism (P=.003). An increase in the volume of the most searched anxiety themes was associated with a reduction in the volume of the remaining themes in 13 countries and an increase in 17 countries, and these 30 countries had a lower prevalence of mental disorders (P<.001) than the countries where no correlations were found., Conclusions: Internet search data could be a potential source for predicting the country-level prevalence of anxiety disorders, especially in understudied populations or when an in-person survey is not viable., (©Barnabas James Gilbert, Chunling Lu, Elad Yom-Tov. Originally published in JMIR Formative Research (https://formative.jmir.org), 22.03.2023.)

Previous studies have revealed that Internet search data is a new source of data that can be used to predict the stock market. In this new, data-driven research field, choosing a method for preprocessing data is crucial to achieving accurate prediction performance. This paper proposes a preprocessing method of Internet search data: composite leading search index (CLSI), which is composed of three steps: (a) keyword selection, (b) time difference measurement, and (c) leading index composition. We demonstrate the validity of CLSI by comparing this method's results with the results from search volume index (SVI), which is most commonly used in previous literatures. We build a time series model (TS) with error correction and support vector regression (SVR) for stock trend prediction, and combine into four models for comparison: SVI-TS, CLSI-TS, SVI-SVR, and CLSI-SVR. We test these four models in the context of the Chinese stock market, which interests more and more investors nowadays, and analyzed results in nine datasets: stable periods, peak periods and trough periods of Shanghai Composite Index, Shenzhen Composite Index, and Hushen 300 index respectively. The results show that using TS and SVR as forecasting models, CLSI performs better than SVI on majority of the test dataset while has almost the same performance with that of SVI on the remaining test dataset. It is to some extent convincing that CLSI is a more efficient preprocessing method of Internet search data for stock trend prediction. [ABSTRACT FROM AUTHOR]

With the rapid development of the internet, the application of internet search data has been seen as a novel data source to offer timely infectious disease surveillance intelligence. Moreover, the advancements in internet search data, which include rich information at both space and time scales, enable investigators to sufficiently consider the spatiotemporal uncertainty, which can benefit researchers to better monitor infectious diseases and epidemics. In the present study, we present the necessary groundwork and critical appraisal of the use of internet search data and spatiotemporal analysis approaches in infectious disease surveillance by updating the current stage of knowledge on them. The study also provides future directions for researchers to investigate the combination of internet search data with the spatiotemporal analysis in infectious disease surveillance. Internet search data demonstrate a promising potential to offer timely epidemic intelligence, which can be seen as the prerequisite for improving infectious disease surveillance., Competing Interests: HS, YZ, GG, and DW were employed by Popsmart Technology (Zhejiang) Co., Ltd., (Copyright © 2022 Sun, Zhang, Gao and Wu.)

The alt-right is a white supremacist social movement that operates primarily online. Its broader constituency has not been studied systematically. Participants in white supremacist movements tend to join in response to threats to their social and economic status. Quantitative work suggests they come primarily from working- and lower-middle class backgrounds. Alt-right leadership, however, argues their movement successfully mobilizes a more affluent population of college-educated professionals. In this paper, we examine predictors of county-level Internet search volume for alt-right content. Results indicate that counties with larger percentages of college graduates, of highly educated non-white and immigrant groups, and higher poverty levels for college graduates tend to have a higher search volume for alt-right content. We interpret this as evidence that the alt-right appeals to college-educated whites experiencing real or perceived threats to their economic and social status. [ABSTRACT FROM AUTHOR]