Baidu Index Research Articles

Analysis of Factors Affecting Voluntourism Network Attention in China: An empirical Test Based on Big Data Analysis

Based on the Baidu index platform, this study takes panel data of 31 provinces, autonomous regions and municipality directly under the central government in China from 2019 to 2023 as a research sample, and and uses big data to empirically analyze the influencing factors of the Voluntourism Network Attention in China. This study chose the Voluntourism Network Attention as the explanatory variable, the seven indicators including the consumption level of the residents, the population above the college and the total tourism revenue of the region as explained variables. Using the STATA15.0 software, the big data model is determined by a descriptive test, a unit root test, a co-integration test, a variance expansion factor (VIF) test, etc. The regression equation of this study was constructed by the generalized least squares method. The empirical study is conducted to determine the influence mode and degree of each factor on the public interest tourism network attention and analyzes the internal causes. The results show that the educational level of the region, the level of economic development and the level of tourism development have a significant positive impact on the Voluntourism Network Attention, and the regression coefficient of the education level and economic development level is the largest. Both of them are the main factors of network the Voluntourism Network Attention. In view of the empirical results, this paper puts forward the following suggestions: First, it is necessary to create more voluntourism routes suitable for college students. Second, it is essential to increase the marketing and promotion of voluntourism activities in economic and tourism developed areas.Last, there is a need to innovate the connotation and form of the voluntourism.

Tracking Chinese Online Activity and Interest in Osteoporosis Using the Baidu Index.

Introduction China's most widely used online search engine, Baidu (Baidu, Inc., Beijing, China), has developed a data collection and analysis tool called the Baidu Index for tracking Internet search trends. The purpose of this study was to examine the utility of the Baidu Index in tracking online osteoporosis information-seeking behavior and comprehending the traits and concerns of the Chinese population. Methods We used the search term "osteoporosis" for the Baidu Index for the years 2018-2022.The geographic and demographic distributions, search volumes, and demand maps were recorded. Results The popularity of the search term "osteoporosis" has increased over time. The search was mostly conducted among women aged 20-39 in northern China. The demand map revealed that the most significant concerns are related to the diagnosis, treatment, and etiology of osteoporosis. Conclusion The Baidu Index is a valuable tool for tracking online health information-seeking behavior among Chinese netizens. Online search trend data appears to reflect the geographic and demographic aspects of osteoporosis to a certain extent.

Using the Baidu Index to Understand the Public Online Interest Towards Moral Education in Mainland China

Using the Baidu Index to Understand the Public Online Interest Towards Moral Education in Mainland China

Using Baidu Index to Track Online Interest in Ethnic Dance

Using Baidu Index to Track Online Interest in Ethnic Dance

Using Baidu Index to Evaluate the Online Attention of Law on the Standard Spoken and Written Chinese Language

Using Baidu Index to Evaluate the Online Attention of Law on the Standard Spoken and Written Chinese Language

Increasing Interest in Marathon in the Context of Building Modern Leading Sporting Nation: An Analysis of Baidu Index Data

Increasing Interest in Marathon in the Context of Building Modern Leading Sporting Nation: An Analysis of Baidu Index Data

Analysis of market risk volatility and warning in carbon trading market

A good risk warning model is an effective way to guarantee the stable operation of the carbon market and exert its emission reduction function. This paper takes the China's pilot carbon market as the research object and measures the carbon market risk by ARMA-GARCH-VaR. Then, influential factors are selected as non-market characteristics from the perspective of causality and incorporated into the risk warning model, and SMOTE and SMOTEENN are introduced to deal with the unbalanced samples. Results show that (1) The risk fluctuations of Beijing and Shenzhen carbon market are large, and the risk fluctuations of Guangdong carbon market are relatively smooth in the early period and larger in the recent period. (2) Air quality, average daily temperature, CSI 300, power coal, natural gas, European carbon market, exchange rate, and Baidu index are all important factors in the risk volatility of carbon market. (3) The Classification Decision Tree (CDT) has the best performance. CDT has the best performance in Guangdong carbon market, SMOTE-CDT has the best performance in Hubei carbon market, and SMOTEENN-CDT has the best performance in Shanghai carbon market, in which SMOTEENN-CDT has a 76% Recall. The non-equilibrium treatment improves the overall Recall, and the risk warning model after non-equilibrium treatment can avoid missing important risk points. This paper can provide a certain degree of reference for the improvement of carbon trading market and risk management.

Financial technology as a heterogeneous driver of carbon emission reduction in China: Evidence from a novel sparse quantile regression

China is facing tremendous pressure to reduce carbon emissions. This paper investigates the impact of financial technology (fintech) on carbon emissions reduction across Chinese provincial regions using annual data from 2011 to 2021. To comprehensively reflect the development level of fintech, we utilize web crawler technology and word frequency analysis to create new variables for measuring the technology level and innovation capability, with fintech-related keywords sourced from the Baidu index. Subsequently, we construct a fintech index for each province in China. Given the heterogeneous relationship between fintech and carbon emissions reduction, we employ an innovative sparse support vector quantile regression to explore the impact of fintech on carbon emissions reduction at different quantile levels. This method employs an Lp-norm regularization term to identify the key variables influencing carbon emission reduction. The empirical results indicate that fintech significantly reduces carbon emissions in all Chinese provincial regions. Furthermore, the positive effects of fintech on carbon emissions reduction are much stronger in high-carbon provinces than in low-carbon provinces. Consequently, it is imperative for policy-makers and practitioners to focus on increasing investment in digital infrastructure and enhancing digital services, particularly in Shanxi, Hebei, Henan, Shaanxi, Ningxia, and Xinjiang provinces. The mechanism analysis reveals that fintech contributes to carbon emission reduction by promoting industrial structure upgrading in more economically developed provinces, while its mediating role is absent in less developed provinces.

Does investor attitude toward carbon neutrality affect stock returns in China?

As climate risk becomes a severe problem, carbon neutrality is attracting the attention of both government and residents, and whether attitude toward carbon neutrality affects the stock market needs to be investigated. Investor attitude was measured by the search frequency and news attention from the Baidu Index. Using a comprehensive daily dataset of Chinese listed firms, the evidence showed that stock prices positively respond to investor attitude. Due to higher exposure on social media, stock turnover rates significantly increased, but institutional ownership significantly decreased. However, stock liquidity hardly improved, suggesting that the firms' fundamental performance was weakly driven by more attention. Moreover, stock returns rose in response to positive news and decreased in response to negative news. Besides, the attention had a larger effect on stocks with higher trading value. The results were robust when using an instrumental variable and an alternative measure. This paper provided suggestions for listed companies and investors. Investor attitudes would increase the stock returns, but the main role was to hype hot concepts. Attention was a double-edged sword, and firms needed to improve their competitive strength when positive attention was high.

Investigating the epidemiological relevance of secretory otitis media and neighboring organ diseases through an Internet search.

This study examined the epidemiological correlations between secretory otitis media (SOM) and diseases of neighboring organs. We measured changes in disease incidences during the 2020 COVID-19 pandemic using Internet big data spanning from 2011 to 2021. This study used the Baidu Index (BI) to determine the search volume for the terms "secretory otitis media (SOM)", "tonsillitis", "pharyngolaryngitis", "adenoid hypertrophy (AH)", "nasopharyngeal carcinoma (NPC)", "nasal septum deviation (NSD)", "rhinosinusitis", "allergic rhinitis (AR)", and "gastroesophageal reflux disease (GERD)" in Mandarin from January 2011 to December 2021. The correlations between these terms were analyzed using Spearman's correlation coefficients. The results were compared search data from 2019 and 2021 to assess the effects of isolation on SOM in 2020. The seasonal variations trends of SOM and other diseases coincided well (P<0.05), except for AR. During the 11-year timeframe, the monthly searches for rhinosinusitis, NSD, tonsillitis, pharyngolaryngitis, and NPC were statistically correlated with SOM (R=0.825, 0.594, 0.650, 0.636, 0.664, respectively; P<0.05). No correlation was found between SOM and AR, SOM and AH, or SOM and GERD (R=-0.028, R=0.259, R=0.014, respectively, P>0.05). The total search volumes for SOM, rhinosinusitis, NPC, and AH decreased in 2020 compared to 2019. SOM exhibited a discernible epidemiological connection with rhinosinusitis, nasal septal deviation (NSD), tonsillitis, pharyngolaryngitis, and nasopharyngeal carcinoma (NPC). A decrease in public gatherings was observed to effectively reduce the incidences of SOM. This underscores the pivotal role of social measures in influencing the prevalence of SOM and emphasizes the intricate interplay between SOM and various associated health factors, with implications for public health strategies.

An intelligent prediction model of epidemic characters based on multi‐feature

AbstractThe epidemic characters of Omicron (e.g. large‐scale transmission) are significantly different from the initial variants of COVID‐19. The data generated by large‐scale transmission is important to predict the trend of epidemic characters. However, the results of current prediction models are inaccurate since they are not closely combined with the actual situation of Omicron transmission. In consequence, these inaccurate results have negative impacts on the process of the manufacturing and the service industry, for example, the production of masks and the recovery of the tourism industry. The authors have studied the epidemic characters in two ways, that is, investigation and prediction. First, a large amount of data is collected by utilising the Baidu index and conduct questionnaire survey concerning epidemic characters. Second, the β‐SEIDR model is established, where the population is classified as Susceptible, Exposed, Infected, Dead and β‐Recovered persons, to intelligently predict the epidemic characters of COVID‐19. Note that β‐Recovered persons denote that the Recovered persons may become Susceptible persons with probability β. The simulation results show that the model can accurately predict the epidemic characters.

Volatility forecasting for stock market incorporating media reports, investors' sentiment, and attention based on MTGNN model

AbstractIn this paper, the self‐monitoring learning model FinBERT is used to identify text emotions, and the sliding time window time‐lagged cross‐correlation (WTLCC) method is utilized to screen Baidu Index keywords for the Shanghai Stock Exchange Index and 18 A‐share listed companies. There are five different types of indicators constructed: news media sentiment, public attention, investor sentiment, investor sentiment disagreement, and media sentiment disagreement. To accurately describe the structure of sentimental contagion, this paper combines graph neural network to learn and output the sentimental contagion graph, and then constructs multivariable time series forecasting with graph neural networks (MTGNN) volatility forecasting model, which can extract the spatial–temporal dependence of variables in pairs. The results show that MTGNN model possesses the highest forecasting accuracy, which performs 30.30% lower on average across four evaluation indicators for Shanghai Stock Exchange Index than temporal pattern attention–long short‐term memory model, which ranks second. For all of the models considered in this paper, adding sentimental contagion mechanism can significantly improve the volatility forecasting accuracy. The error of MTGNN is reduced the most, with a 15.21% average reduction for the Shanghai Stock Exchange Index. The contagion relationship among media reports, investor sentiment, and attention can help provide new ideas for enhancing the precision of volatility forecasting from the public opinion environment in the financial market.

Development of a novel dynamic nosocomial infection risk management method for COVID-19 in outpatient settings

BackgroundApplication of accumulated experience and management measures in the prevention and control of coronavirus disease 2019 (COVID-19) has generally depended on the subjective judgment of epidemic intensity, with the quality of prevention and control management being uneven. The present study was designed to develop a novel risk management system for COVID-19 infection in outpatients, with the ability to provide accurate and hierarchical control based on estimated risk of infection.MethodsInfection risk was estimated using an auto regressive integrated moving average model (ARIMA). Weekly surveillance data on influenza-like-illness (ILI) among outpatients at Xuanwu Hospital Capital Medical University and Baidu search data downloaded from the Baidu Index in 2021 and 22 were used to fit the ARIMA model. The ability of this model to estimate infection risk was evaluated by determining the mean absolute percentage error (MAPE), with a Delphi process used to build consensus on hierarchical infection control measures. COVID-19 control measures were selected by reviewing published regulations, papers and guidelines. Recommendations for surface sterilization and personal protection were determined for low and high risk periods, with these recommendations implemented based on predicted results.ResultsThe ARIMA model produced exact estimates for both the ILI and search engine data. The MAPEs of 20-week rolling forecasts for these datasets were 13.65% and 8.04%, respectively. Based on these two risk levels, the hierarchical infection prevention methods provided guidelines for personal protection and disinfection. Criteria were also established for upgrading or downgrading infection prevention strategies based on ARIMA results.ConclusionThese innovative methods, along with the ARIMA model, showed efficient infection protection for healthcare workers in close contact with COVID-19 infected patients, saving nearly 41% of the cost of maintaining high-level infection prevention measures and enhancing control of respiratory infections.

The prediction of influenza-like illness using national influenza surveillance data and Baidu query data

BackgroundSeasonal influenza and other respiratory tract infections are serious public health problems that need to be further addressed and investigated. Internet search data are recognized as a valuable source for forecasting influenza or other respiratory tract infection epidemics. However, the selection of internet search data and the application of forecasting methods are important for improving forecasting accuracy. The aim of the present study was to forecast influenza epidemics based on the long short-term memory neural network (LSTM) method, Baidu search index data, and the influenza-like-illness (ILI) rate.MethodsThe official weekly ILI% data for northern and southern mainland China were obtained from the Chinese Influenza Center from 2018 to 2021. Based on the Baidu Index, search indices related to influenza infection over the corresponding time period were obtained. Pearson correlation analysis was performed to explore the association between influenza-related search queries and the ILI% of southern and northern mainland China. The LSTM model was used to forecast the influenza epidemic within the same week and at lags of 1–4 weeks. The model performance was assessed by evaluation metrics, including the mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE).ResultsIn total, 24 search queries in northern mainland China and 7 search queries in southern mainland China were found to be correlated and were used to construct the LSTM model, which included the same week and a lag of 1–4 weeks. The LSTM model showed that ILI% + mask with one lag week and ILI% + influenza name were good prediction modules, with reduced RMSE predictions of 16.75% and 4.20%, respectively, compared with the estimated ILI% for northern and southern mainland China.ConclusionsThe results illuminate the feasibility of using an internet search index as a complementary data source for influenza forecasting and the efficiency of using the LSTM model to forecast influenza epidemics.

Assessing the nonlinear relationship between consumer goods and water pollution in different seasons with machine learning models: A case study in the Yangtze River Economic Belt

Consumer goods volume and their consumption structure significantly contribute to water pollution within economic zones. However, there remains a need to better comprehend the nuanced and seasonal nonlinear relationship between consumer goods and water pollution, which currently obstructs essential consumption adjustments aimed at reducing water pollution. This study selected an effective machine learning model Light Gradient Boosting Machine (LightGBM) with SHapley Additive Explanations (SHAP) to assess the nonlinear relationship between consumer goods and water pollution during flood and non-flood seasons within the Yangtze River Economic Belt from 2013 to 2022, Baidu Index of consumer goods were used to represent the potential consumption level (PCL). The findings revealed the following: (1) During flood seasons, hydroelectricity and the PCL of furniture and cultural goods emerged as factors driving water pollution reduction. Conversely, GDP, PCL of communication devices, and beverages played significant roles in exacerbating water pollution. During non-flood seasons, the drivers for reducing water pollution included GDP, income gap and PCL of cultural goods. Meanwhile, PCL of cars, western medicine and oil became pivotal drivers for exacerbating water pollution. (2) Concerning consumption structure during flood and non-flood seasons, maintaining consumption levels of costume industrial goods, cultural, sports and health goods and transportation and accommodation goods below 66.71%, 77.66% and 79.61% respectively of their average PCL appeared beneficial in reducing water pollution. During flood seasons, consumption levels of cars and furniture below 85.69% and 91.46% respectively of their average PCL were associated with reduced water pollution. During non-flood seasons, consumption levels of cars and oil below 82.95% and 71.28% respectively of their average PCL could help reduce water pollution. (3) The proportion of hydroelectricity generation exceeding 2% and 3% respectively during flood and non-flood seasons helped in reducing water pollution. Moreover, GDP per capita surpassing 2388.82 and 2075.78 respectively during flood and non-flood season was preferable for reducing water pollution. These findings deepened our understanding of the nonlinear relationship and identify promising consumer goods and their consumption structure that could effectively aid in reducing water pollution from a consumption perspective.

Temporal and Spatial Analysis of Negative Emotions in China during the COVID-19 Pandemic.

This research aims to explore the spatiotemporal distribution patterns of negative emotions in mainland China during different stages of the COVID-19 pandemic and the external factors influencing this clustering. Using Baidu Index data for 91 negative emotion keywords, a retrospective geographic analysis was conducted across Chinese provinces from 14 October 2019 to 7 July 2022. Four spatial analysis methods (Global Moran's Index, Local Moran's Index, Bivariate Global Moran's Index, and Bivariate Local Moran's Index) are employed to identify potential clustering patterns and influencing factors of negative emotions at different stages. The results indicate that the COVID-19 pandemic significantly intensified the clustering effect of negative emotions in China, particularly with a more pronounced radiation effect in northwestern provinces. Spatial positive correlations are observed between pandemic-related Baidu indices (pandemic Baidu index, government Baidu index, nucleic acid Baidu index) and negative emotions. These findings contribute to understanding the spatiotemporal distribution characteristics of negative emotions in China post the COVID-19 outbreak and can guide the allocation of psychological resources during emergencies, thereby promoting social stability.

Online public concern about allergic rhinitis and its association with COVID-19 and air quality in China: an informative epidemiological study using Baidu index

BackgroundAllergic rhinitis is a common health concern that affects quality of life. This study aims to examine the online search trends of allergic rhinitis in China before and after the COVID-19 epidemic and to explore the association between the daily air quality and online search volumes of allergic rhinitis in Beijing.MethodsWe extracted the online search data of allergic rhinitis-related keywords from the Baidu index database from January 23, 2017 to June 23, 2022. We analyzed and compared the temporal distribution of online search behaviors across different themes of allergic rhinitis before and after the COVID-19 pandemic in mainland China, using the Baidu search index (BSI). We also obtained the air quality index (AQI) data in Beijing and assessed its correlation with daily BSIs of allergic rhinitis.ResultsThe online search for allergic rhinitis in China showed significant seasonal variations, with two peaks each year in spring from March to May and autumn from August and October. The BSI of total allergic rhinitis-related searches increased gradually from 2017 to 2019, reaching a peak in April 2019, and declined after the COVID-19 pandemic, especially in the first half of 2020. The BSI for all allergic rhinitis themes was significantly lower after the COVID-19 pandemic than before (all p values < 0.05). The results also revealed that, in Beijing, there was a significant negative association between daily BSI and AQI for each allergic rhinitis theme during the original variant strain epidemic period and a significant positive correlation during the Omicron variant period.ConclusionBoth air quality and the interventions used for COVID-19 pandemic, including national and local quarantines and mask wearing behaviors, may have affected the incidence and public concern about allergic rhinitis in China. The online search trends can serve as a valuable tool for tracking real-time public concerns about allergic rhinitis. By complementing traditional disease monitoring systems of health departments, these search trends can also offer insights into the patterns of disease outbreaks. Additionally, they can provide references and suggestions regarding the public’s knowledge demands related to allergic rhinitis, which can further be instrumental in developing targeted strategies to enhance population-based disease education on allergic diseases.

Deep evolutionary fusion neural network: a new prediction standard for infectious disease incidence rates

BackgroundPreviously, many methods have been used to predict the incidence trends of infectious diseases. There are numerous methods for predicting the incidence trends of infectious diseases, and they have exhibited varying degrees of success. However, there are a lack of prediction benchmarks that integrate linear and nonlinear methods and effectively use internet data. The aim of this paper is to develop a prediction model of the incidence rate of infectious diseases that integrates multiple methods and multisource data, realizing ground-breaking research.ResultsThe infectious disease dataset is from an official release and includes four national and three regional datasets. The Baidu index platform provides internet data. We choose a single model (seasonal autoregressive integrated moving average (SARIMA), nonlinear autoregressive neural network (NAR), and long short-term memory (LSTM)) and a deep evolutionary fusion neural network (DEFNN). The DEFNN is built using the idea of neural evolution and fusion, and the DEFNN + is built using multisource data. We compare the model accuracy on reference group data and validate the model generalizability on external data. (1) The loss of SA-LSTM in the reference group dataset is 0.4919, which is significantly better than that of other single models. (2) The loss values of SA-LSTM on the national and regional external datasets are 0.9666, 1.2437, 0.2472, 0.7239, 1.4026, and 0.6868. (3) When multisource indices are added to the national dataset, the loss of the DEFNN + increases to 0.4212, 0.8218, 1.0331, and 0.8575.ConclusionsWe propose an SA-LSTM optimization model with good accuracy and generalizability based on the concept of multiple methods and multiple data fusion. DEFNN enriches and supplements infectious disease prediction methodologies, can serve as a new benchmark for future infectious disease predictions and provides a reference for the prediction of the incidence rates of various infectious diseases.

Assessment of environmental concern for enterprise pollution reduction

Environmental regulation produces different effects in different regions. Public concern for the environment in different regions may affect the effectiveness of environmental regulation. Using the pollution emission data of the Chinese Industrial Enterprise Database and the Baidu Index, this paper uses econometric models to evaluate the effect of public environmental concern on enterprise pollution emissions. Results show that public environmental concern reduces enterprise emissions with a coefficient of -3.026. Further research shows that the negative relationship between public environmental concern and pollution emissions is significant for state-owned enterprises, longer-established enterprises, and enterprises in cities with high GDP, in populated areas, and in cities with a higher concentration of secondary industries. Through mechanism analysis, we found that the public can urge enterprises to reduce emissions through centralized treatment of wastewater, harmless treatment of garbage, and increasing green areas, and enterprises should take corresponding measures for different emissions. Finally, we present suggestions for pluralistic co-governance from the perspectives of the government, enterprises, and the media.

Exploring the Lagged Correlation Between Baidu Index and Influenza-Like Illness - China, 2014-2019.

This study investigated the lagged correlation between Baidu Index for influenza-related keywords and influenza-like illness percentage (ILI%) across regions in China. The aim is to establish a scientific foundation for utilizing Baidu Index as an early warning tool for influenza-like illness epidemics. In this study, data on ILI% and Baidu Index were collected from 30 provincial-level administrative divisions (PLADs) spanning April 2014 to March 2019. The Baidu Index was categorized into Overall Index, Ordinary Index, Prevention Index, Symptom Index, and Treatment Index based on search query themes. The lagged correlation between the Baidu Index and ILI% was examined through the cross-correlation function (CCF) method. Correlating the Baidu Overall Index of 30 PLADs with ILI% revealed CCF values ranging from 0.46 to 0.86, with a median lag of 0.5 days. Subcategory analysis indicated that the Prevention Index and Symptom Index exhibited quicker responses to ILI%, with median lags of -9 and -0.5 days, respectively, compared to 0 and 3 days for the Ordinary and Treatment Indexes. The median lag days between the Baidu Index and the ILI% were earlier in the northern PLADs compared to the southern PLADs. The Prevention and Symptom Indexes show promising predictive capabilities for influenza-like illness epidemics.