Web Search Data Research Articles

A Novel Approach for Forecasting Tourist Arrivals Using Web Search Data and Artificial Intelligence

The development of economic activity has been matched by growth in the tourism industry. According to information, the tourism industry is growing and both the number of domestic and international tourists visiting each year is expanding. Because of this quick expansion, there are now critical complications with the management of tourism, such as predicting the arrivals for travel, particularly when a lot of people are visiting appealing locations for particular periods. The proposed Artificial Fish Swarm Optimized Dynamic Gated Recurrent Unit (AFSO-DGRU) approach transforms the forecasting of demand for tourism by utilizing intelligence from swarms to improve forecasts and strategically adapting to fluctuating visitor structures. It ensures accurate and dynamic responses even during times of uncertainty when demand is high. The study used Google Trends to collect data from searches on the web and examine trends in tourist’s interest and demand for travel. By combining innovative artificial intelligence (AI) algorithms with realtime online search data, this study presents a novel way to improve the accuracy of visitor arrival predictions. The proposed method performs better than the existing methods to utilize the parameters such as mean absolute deviation called MAE (42.01), mean square error denoted by MSE (3059.85), mean absolute percentage error defined MAPE (1.34), and RMSPE or root mean square percentage error (1.43). This research utilizes web search data and AI to improve the accuracy of forecasting tourist arrivals, offering valuable insights for understanding tourism trends.

Identifying sarcoidosis trends using web search and real-world data in Sweden: a retrospective longitudinal study

Web search data are associated with disease incidence, population interest, and seasonal variations. This study aimed to investigate seasonal and geographical variations of web search data for sarcoidosis and to explore its association with external factors and sarcoidosis incidence in Sweden. Therefore, sarcoidosis-related data from Google Ads Keyword Planer (2017–2020) were generated for Sweden according to its 21 counties. The relationship between search volume and season, region, population demographics, environmental factors, and the sarcoidosis incidence listed in the National Patient Register was assessed. Analyses revealed seasonal variations for Sweden with an overall peak in the spring and autumn. Geographical differences were observed, with a higher search volume for north-western counties and the lowest search volume for Stockholm County. At the country level, the search volume was positively associated with the sarcoidosis incidence. Higher male proportion and older mean age were associated with a higher search volume, while a higher proportion of foreign-born residents, humidity, and mean temperature were associated with a lower search volume. Our analyses detected correlations between web search data, sarcoidosis incidence, and external factors. Analyses of sarcoidosis web search data therefore appear to be a valuable approach to disease surveillance to address medical needs and public interest.

Short- and long-term destination resilience in Wuhan, China under COVID-19: Differences in scenic spot type and origin market structure

Analyzing changes in destination resilience can inform critical but under-explored linkages characterizing disaster events and recovery. Based on web search data from 2011 to 2021, this study applied Tourism Background Trend Line (TBTL) modeling to calculate tourism resilience of 41 scenic spots in Wuhan, China under COVID-19. Existing government classifications and market demand data informed scenic spot categorization into three groups based on attraction type (cultural, leisure, natural) or domestic origin market structure (high, moderate and low dependence on the local market). Two-way ANOVAs tested differences in short- and long-term resilience among these different groups. Results indicate that scenic spots with low dependence on the local market were most resilient in the short-term (Jan–April 2020), while scenic spots with high dependence on the local market and natural scenic spots were most resilient in the long-term (May 2020–Dec 2021). Findings highlight heterogeneities in destination resilience at the micro-scale within a single destination, encouraging managers to develop targeted marketing strategies for various scenic spots at different stages of crises like COVID-19. The study's novel framework and implications also inform short-versus long-term resilience theory while accentuating destination recovery strategies in post-pandemic contexts.

Burden of atopic dermatitis in Europe: A population-centred approach leveraging web search data in 21 European countries.

The complexity, high prevalence, and substantial personal and socioeconomic burden collectively render atopic dermatitis (AD) a major public health concern. Using crowdsourced Internet data has the potential to provide unique insights into this concern, as demonstrated by several previous studies. However, a comprehensive comparison across European countries remains lacking. The study aimed to investigate AD-related web searches across Europe to assess spatiotemporal variations and associations between disease-related and external factors. AD-related web search data were extracted for 21 European countries between February 2019 and January 2023. Descriptive analysis and autocorrelation functions were performed to examine spatiotemporal patterns. Correlations (r) were used to evaluate the associations between web searches and disease-related, socioeconomic and meteorological data. Over 241 million AD-related web searches were identified, with search volume varying substantially among European countries (p < 0.001) and correlating with AD prevalence and disease burden (both r = 0.51, p = 0.019). Search volume increased between 2019 and 2023 in all countries and seasonally peaked in January and March. Negative correlations with median population age (r = -0.46, p = 0.039), number of general practitioners (r = -0.29, p = 0.226) and specialists (r = -0.27, p = 0.270) were observed. Moderate to strong correlations were found between search volume and cold, humid and windy weather with fewer sunshine hours, while higher online interest typically occurred 1-3 months after such weather conditions. The study highlights the great potential of online crowdsourced data analysis, for example, to investigate the impact of climate change or to identify unmet needs at a population level. Furthermore, the growing online interest in AD and the corresponding seasonal peaks emphasize the necessity of adapting treatment plans, intensifying public health campaigns, and disseminating reliable online information by governments and healthcare providers, especially during these periods.

Change of perspective: impact of COVID-19 pandemic on axial spondyloarthritis-related web searches in Germany.

Several conventional cross-sectional studies have investigated the impact of the coronavirus disease (COVID-19) pandemic on patients with axial spondyloarthritis (axSpA) and reached contrary results regarding health and well-being. As analysis of web search data already provided insights into public interest and unmet needs, this study aimed to examine axSpA-related web searches before and during COVID-19 pandemic to gain a different perspective on the impact of COVID-19 on this disease. The Google Ads Keyword Planner was used to generate axSpA-related keywords and their monthly number of searches between June 2018 and November 2021 in Germany. These keywords were qualitatively classified into seven categories. A total of 538 axSpA-related keywords were used for the analysis. The number of axSpA-related searches increased during COVID-19 pandemic (before: n = 1,525,010 vs. during: n = 1,848,300), particularly searches for symptoms, disease outcomes, and causes, while interest in disease management and diagnosis decreased. This study demonstrated a shift in public interest in axSpA during COVID-19 in Germany and highlights an urgent expansion of telemedicine to be prepared for exceptional situations such as a pandemic.

Analysing the impact of comorbid conditions and media coverage on online symptom search data: a novel AI-based approach for COVID-19 tracking

Background Web search data have proven to bea valuable early indicator of COVID-19 outbreaks. However, the influence of co-morbid conditions with similar symptoms and the effect of media coverage on symptom-related searches are often overlooked, leading to potential inaccuracies in COVID-19 simulations. Method This study introduces a machine learning-based approach to estimate the magnitude of the impact of media coverage and comorbid conditions with similar symptoms on online symptom searches, based on two scenarios with quantile levels 10–90 and 25–75. An incremental batch learning RNN-LSTM model was then developed for the COVID-19 simulation in Australia and New Zealand, allowing the model to dynamically simulate different infection rates and transmissibility of SARS-CoV-2 variants. Result The COVID-19 infected person-directed symptom searches were found to account for only a small proportion of the total search volume (on average 33.68% in Australia vs. 36.89% in New Zealand) compared to searches influenced by media coverage and comorbid conditions (on average 44.88% in Australia vs. 50.94% in New Zealand). The proposed method, which incorporates estimated symptom component ratios into the RNN-LSTM embedding model, significantly improved COVID-19 simulation performance. Conclusion Media coverage and comorbid conditions with similar symptoms dominate the total number of online symptom searches, suggesting that direct use of online symptom search data in COVID-19 simulations may overestimate COVID-19 infections. Our approach provides new insights into the accurate estimation of COVID-19 infections using online symptom searches, thereby assisting governments in developing complementary methods for public health surveillance.

Leveraging web search data in Germany to identify unmet needs of contraceptives on a population-based level: A longitudinal retrospective study.

There are a variety of possible contraceptives available. While medical advice is an important resource for selecting the individual contraceptive, previous research has shown that the Internet has become an increasingly important source of health care information. This study aims to identify key trends in contraception-related web searches in Germany and thus allows conclusions about preferences and unmet needs with regard to pregnancy prevention. Longitudinal retrospective study. Google Ads Keyword Planner was used to identify contraception-related keywords and their search volume in Germany and all federal states between 2018 and 2021. The keywords were categorized based on gender, hormonal/non-hormonal, and different contraceptive methods. Search volume and categories were analyzed for temporal trends, regional differences, and underlying socioeconomic variables. The 1481 contraception-related keywords corresponded to 15,081,760 searches. In total, a 56% increase in searches/100,000 inhabitants was observed. Highest mean search volume was observed in categories "woman," "woman/non-hormonal" and "woman/non-hormonal/barrier," respectively, and in the federal state Hamburg, while the lowest was seen in North Rhine-Westphalia. The increase in search volume reflects a high interest in contraception, particularly in non-hormonal female methods. This stands in contrast to the limited number of effective non-hormonal contraceptives available and points to an unmet need. In addition, the low search volume for male contraceptives demonstrates gender-specific responsibilities regarding family planning in German society.

Anti-Immigrant Rhetoric and ICE Reporting Interest: Evidence from a Large-Scale Study of Web Search Data

Abstract This paper studies whether media cues can motivate interest in reporting suspected unauthorized immigrants to Immigration and Customs Enforcement (ICE). Using web search data and automated content analysis of cable news transcripts, we examine the role of media coverage on searches for how to report immigrants to ICE and searches about immigrant crime and welfare dependency. We find significant and persistent increases in news segments on crime by after Trump's inauguration, accompanied by a sharp increase in searches for how to report immigrants. We find a strong association between daily reporting searches and immigration and crime coverage. Using searches during broadcasts of presidential speeches, we isolate the specific effect of anti-immigrant media coverage on searches for how to report immigrants to ICE. The findings indicate that the media's choices regarding the coverage of immigrants can have a strong impact on the public's interest in behaviour that directly harms immigrants.

New Horizons in Web Search, Web Data Mining, and Web-Based Applications

In today’s era of rapid digitization and information technology advancement, web search and web data mining stand at the core of the technological progress of numerous web-based applications [...]

Ukrainization and the Effect of Russian Language on the Web: The Google Trends Case Study

ABSTRACT We consider the question of language diffusion and examine the latest attempt to popularize the Ukrainian language. This effort has been undertaken by the Ukrainian government since 1991. While sociological studies report positive dynamics in Ukrainian language dissemination, in practice, the language adoption progress might be slower than reported. We test this hypothesis using google trends web search data. We apply a Bayesian beta regression model and show that the proposed model is appropriate. The results suggest that the Ukrainian language popularization policy is successful and that the 2022 Russian invasion has considerably intensified the usage of the Ukrainian language.

Nowcasting tourist nights spent using innovative human mobility data.

The publication of tourism statistics often does not keep up with the highly dynamic tourism demand trends, especially critical during crises. Alternative data sources such as digital traces and web searches represent an important source to potentially fill this gap, since they are generally timely, and available at detailed spatial scale. In this study we explore the potential of human mobility data from the Google Community Mobility Reports to nowcast the number of monthly nights spent at sub-national scale across 11 European countries in 2020, 2021, and the first half of 2022. Using a machine learning implementation, we found that this novel data source is able to predict the tourism demand with high accuracy, and we compare its potential in the tourism domain to web search and mobile phone data. This result paves the way for a more frequent and timely production of tourism statistics by researchers and statistical entities, and their usage to support tourism monitoring and management, although privacy and surveillance concerns still hinder an actual data innovation transition.

An interval-valued carbon price forecasting method based on web search data and social media sentiment.

Accurate carbon price prediction is a crucial task for the carbon trading market. Previous studies have ignored the impact of online data and are limited to point predictions, which brings challenges to the accurate forecasting of carbon prices. To address those issues, this paper proposes an interval-valued carbon price forecasting method based on web search data and social media sentiment. First, we collect web search data and social media sentiment to improve prediction performance by synthesizing multiple types of data information. Second, we employ principal component analysis (PCA) to preprocess high-dimensional web search data, and utilize BosonNLP for quantifying social media information, thereby enhancing the predictability of the dataset. Subsequently, a variational mode decomposition (VMD) is applied to the carbon price and online data, followed by utilizing particle swarm optimization support vector regression (PSO-SVR) to predict each sub-modes and summing them up to obtain the ultimate forecasting outcome. Finally, using carbon prices in Guangdong and Hubei provinces as case studies, the experimental results demonstrate that web search data and social media sentiment significantly enhance the predictive accuracy of interval-valued carbon prices. Furthermore, the proposed VMD-PSO-SVR outperforms other comparative models in the accuracy and reliability of interval-valued forecasting.

Impact of asthma in Europe: A comparison of web search data in 21 European countries

BackgroundAsthma is a chronic inflammatory disorder of the airways and one of the most important non-communicable diseases worldwide. Analyzing crowdsourced data can help understand public interest and unmet needs as well as potential factors influencing search behavior. ObjectiveThe study aimed to investigate asthma-related web search data in Europe to identify possible regional and seasonal variations and to assess public interest. MethodsGoogle Ads Keyword Planner was used to measure search volume for search terms related to asthma, allergic asthma, and bronchial asthma in 21 European countries between January 2018 and December 2021. The top 10 keywords of each country were categorized qualitatively. Search volume per 100 000 inhabitants was descriptively assessed in terms of regional and seasonal trends. Spearman correlations between search volume and pollen concentration as well as coronavirus disease (COVID-19) cases were investigated. ResultsThe median search volume per 100 000 inhabitants for asthma and allergic asthma was highest in Northern and Western Europe, while the highest search volume for bronchial asthma was observed in Western and Eastern regions. A seasonal trend was identified for all search terms and in all regions. Correlations were found between search frequency and pollen load and search behavior and COVID-19 cases. Overall, Europeans were most interested in the diseases in general, their treatment options, and symptoms. ConclusionThese results highlighted the need for reliable and region-specific information about the disease and for public campaigns to improve asthma control. The study also emphasizes the importance of using crowdsourced data for a more encompassing overview beyond conventional healthcare data.

Bird predation on Roseau cane scale as revealed by a web image search and querying a citizen monitoring database

AbstractAn invasion of Roseau cane scale, Nipponaclerda biwakoensis, native to East Asia, is associated with recent widespread dieback of common reed stands in the Mississippi River Delta, Louisiana. A previous study suggested that, in its native range, the combined mortality from Common Reed Bunting Emberiza schoeniclus and parasitoid wasps may control the abundance of Roseau cane scale. However, there is limited knowledge about which other bird species may prey on Roseau cane scale or the feeding patterns of these birds. Here we used web search and citizen science monitoring data to tabulate bird predation events on Roseau cane scales across sites in Japan and determine which bird species may be the most important predators and when the observed feeding typically occurs. We scanned web entries using both Google image searches and the social networking services including Twitter, Instagram, and Facebook. Additionally, we accessed long‐term field observation records obtained by volunteer monitoring conducted by the members of the Kanagawa Branch of the Wild Bird Society of Japan. By using these citizen science approaches, we detected 16 bird species, mainly Common Reed Bunting but also other migratory and resident species, preying on the Roseau cane scale in reed beds throughout central Japan and most detection occurred during the winter. This study demonstrates that biological information obtained from publicly available data from citizen observers can be useful for exploring ecological patterns of various species including potential predators against invasive species.

Web Search Query Volume Correlates With Prescription Volumes of Antidepressants and Antipsychotics in the Netherlands and United Kingdom: An Explorative Study.

The significant increase in Internet availability has resulted in a rise in search queries on health-related topics. Previous research has demonstrated the potential for analyzing web search query volume for nonpsychotropic prescription drugs, while studies on psychotropic drugs remain scarce. The aims of this study were to expand upon this scarce knowledge by investigating the relationship between web search query volumes and prescription volumes of antidepressants and antipsychotics in the United Kingdom and the Netherlands and to gain insight in topics of concern, such as withdrawal symptoms and discontinuation. Data were obtained for the United Kingdom and the Netherlands from January 2010 until January 2021. Prescription volume data for 5 antidepressants (paroxetine, fluoxetine, sertraline, citalopram, venlafaxine) and 5 antipsychotics (quetiapine, olanzapine, clozapine, aripiprazole, and risperidone) were obtained. Web search query volumes and data on related search queries of these substances were acquired from Google Trends. Descriptive statistics and Pearson correlation analyses were performed. A strong, positive, and statistically significant correlation between web search query volume and prescription volume was observed for most included substances in both the Netherlands and the United Kingdom. The search queries related to the included antidepressants and antipsychotics indicate important topics of concern for specific substances, such as withdrawal symptoms and discontinuation. Web search data from Google Trends could potentially be used as a proxy for prescribing trends of antidepressants and antipsychotics and to gain insight in topics of concern of users of these substances. These findings highlight the importance of providing reliable patient information, particularly regarding adverse effects, withdrawal, and discontinuation.

Using Machine Learning Methods to Predict Consumer Confidence from Search Engine Data

The consumer confidence index is a leading indicator of regional socioeconomic development. Forecasting research on it helps to grasp the future economic trends and consumption trends of regional development in advance. The data contained on the Internet in the era of big data can truly and timely reflect the current economic trends. This paper constructs a conceptual framework for the relationship between the consumer confidence index and web search keywords. It employed six machine learning and deep learning models: the BP neural network, the convolutional neural network, support vector regression, random forest, the ELMAN neural network, and the extreme learning machine to predict the consumer confidence index. The study shows that the use of machine learning models has a better prediction effect on the consumer confidence index. Compared with other models, the BP neural network and the convolutional neural network have lower error indicators and higher model accuracy, which helps decision-makers forecast the consumer confidence index. Consumers search for various goods and prices, as well as macroeconomics, to understand the economic conditions of the market, which affects the consumer confidence index and consumption decisions. Therefore, web search data can be used to predict consumer confidence. Future research can be extended to other macro indicator-related prediction studies. It is important to promote market consumption and confidence, improve consumption policies, and promote national prosperity.

Assessing the national burden of allergic asthma by web-search data, pollen counts, and drug prescriptions in Germany and Sweden

BackgroundAsthma and its main phenotype allergic asthma are prevalent, chronic, and complex diseases affecting 4% of the population. One main trigger for allergic asthma exacerbations is pollen. Online health information search behavior by people is increasing, and analysis of web-search data can provide valuable insight into disease burden and risk factors of a population. ObjectivesWe sought to perform a web-search data analysis and correlation to climate factors and pollen in 2 European countries. MethodsWe analyzed the national web-search volume for allergic asthma-related keywords in Germany and Sweden from 2018 to 2021 and correlated it to local pollen counts, climatic factors, and drug prescription rates. ResultsPer capita, more searches were conducted in Sweden than in Germany. A complex geographic stratification within the countries was observed. Search results were seasonal with a peak in spring and correlated with pollen counts in both countries. However, anti-asthmatic drug prescription rates in Sweden, as well as temperature and precipitation in both countries, did not correlate with search volume. ConclusionOur analysis offers population-level insights about this complex disease by reporting its needs and establishing the correlation to pollen counts, which enables a targeted approach in the public health management of allergic asthma. Local pollen counts, as opposed to temperature or precipitation, might be good predictors of allergic asthma disease burden.

Analyzing web searches for axial spondyloarthritis in Germany: a novel approach to exploring interests and unmet needs

Axial spondyloarthritis (axSpA) is an underdiagnosed condition with a high disease burden. Due to delayed diagnosis and limited access to specialist care, conventional health data might not sufficiently capture the perspective of affected individuals. The aim of this study was to assess public interest, unmet needs, and disease burden of axSpA in Germany through the analysis of thematic, geographic, and temporal patterns in national web search data. Google Ads Keyword Planner was used to identify axSpA-related keywords and their monthly search volume in Germany between January 2017 and December 2020. Identified keywords were qualitatively categorized into six categories. Overall, 265 axSpA-related keywords with a search volume of 3,881,490 queries were identified. Nearly 81% of the total search volume was assigned to the category terms and definition, while 19% referred to either outcomes, symptoms, diagnosis, management, or causes. In the category outcomes, prognostic outcomes like “life expectancy” generated more searches than physical manifestations like “pain”. Less populated cities showed significantly more searches per 100,000 inhabitants than larger cities. Searches were seasonally stable with a Germany-wide peak in July 2017. This study provides an overview of public interest in axSpA based on web search data in Germany. The identified search patterns could be used to guide public health campaigns and optimize axSpA management in Germany.

E-Learning Platforms Analysis – Modern Review

E-learning is a novel instructional technique for imparting necessary information and skills to students. This article gives a thorough examination of the current state of e-learning approaches in organizations. The authors also assess the efficacy of e-learning by analyzing the current research articles. This study is presented and evaluated in several models to explain student intention to use an e-learning system as a substitute for traditional classroom learning or as a stand-alone distant education approach. The results of a survey related to eLearning platforms were reviewed by other researchers to determine the identified discoveries and uncover the research gaps. This work is unique as it contributes to the current literature discussion. The article's scientific uniqueness also includes a large-scale investigation that describes the author's theoretical and practical prerequisites. A critical examination of the literature is offered in this paper to establish a more realistic foundation for e-learning success. This paper reviews various e-learning tools and presents visualizations of web search data that display the popularity of electronic learning systems. Keywords: e-learning, Moodle, Blackboard, Top Hat, Google Classroom. DOI: https://doi.org/10.55463/hkjss.issn.1021-3619.60.89

Detecting Elevated Air Pollution Levels by Monitoring Web Search Queries: Algorithm Development and Validation

Real-time air pollution monitoring is a valuable tool for public health and environmental surveillance. In recent years, there has been a dramatic increase in air pollution forecasting and monitoring research using artificial neural networks. Most prior work relied on modeling pollutant concentrations collected from ground-based monitors and meteorological data for long-term forecasting of outdoor ozone (O3), oxides of nitrogen, and fine particulate matter (PM2.5). Given that traditional, highly sophisticated air quality monitors are expensive and not universally available, these models cannot adequately serve those not living near pollutant monitoring sites. Furthermore, because prior models were built based on physical measurement data collected from sensors, they may not be suitable for predicting the public health effects of pollution exposure. This study aimed to develop and validate models to nowcast the observed pollution levels using web search data, which are publicly available in near real time from major search engines. We developed novel machine learning-based models using both traditional supervised classification methods and state-of-the-art deep learning methods to detect elevated air pollution levels at the US city level by using generally available meteorological data and aggregate web-based search volume data derived from Google Trends. We validated the performance of these methods by predicting 3 critical air pollutants (O3, nitrogen dioxide, and PM2.5) across 10 major US metropolitan statistical areas in 2017 and 2018. We also explore different variations of the long short-term memory model and propose a novel search term dictionary learner-long short-term memory model to learn sequential patterns across multiple search terms for prediction. The top-performing model was a deep neural sequence model long short-term memory, using meteorological and web search data, and reached an accuracy of 0.82 (F1-score 0.51) for O3, 0.74 (F1-score 0.41) for nitrogen dioxide, and 0.85 (F1-score 0.27) for PM2.5, when used for detecting elevated pollution levels. Compared with using only meteorological data, the proposed method achieved superior accuracy by incorporating web search data. The results show that incorporating web search data with meteorological data improves the nowcasting performance for all 3 pollutants and suggest promising novel applications for tracking global physical phenomena using web search data.