从Google Trends看“一带一路”倡议的国际反响

[目的/意义]2013年中国提出“一带一路”倡议,这对推动我国继续繁荣发展并为全球提供中国治理体系方案等具有重大意义。[方法/过程]利用Google Trends,分析中国“一带一路”倡议的国际反响及其潜在阻碍因子。[结果/结论]“一带一路”倡议受到国际上绝大多数国家民众的支持和关注;个别国家为了压制中国的国际影响力,通过“债务陷阱外交”“珍珠链战略”以及环境问题等方面抹黑“一带一路”,行动上提出“重建更好世界”等企图替代中国的“一带一路”倡议。得益于“一带一路”倡议投资没有附加条件以及G7国家内部自身问题,“一带一路”倡议仍然获得了广大第三世界国家的支持,并将继续是推动世界经济发展的重要力量。

世界是如何关注电子竞技的?——Google Trends带给电竞的思考

搜索引擎巨鳄——Google近期推出了一项新的服务“Google Trends”,翻译成中文应该叫“谷歌趋势”。在Google的中国网志中是这样描述的,“(Google Trends)让你可以看到近几年人们在Google上搜索某个词或某几个词的趋势,以及在哪些国家、地区、语言上对这些词的搜索量最多。”简言之,通过它,你可以看到这个世界正在搜索什么。为了给读者一个更加全面而有深度的认识,笔者特地翻译了Google官方对“Google Trends”的详细说明,并且根据本杂志的情况做了一些调整,其中部分内容如下:用Google Trends,你能比较世界对你你感兴趣主题的关注度。输入若干主题,并且观察他们一段时间内在Google中被搜索的频率。Google Trends也显示了你的主题在Google资讯组中的出现频率,以及哪些地区搜索它们更加频繁。

世界是如何关注电子竞技的? Google Trends带给电竞的思考

背景:搜索引擎巨鳄——Google在2006年中旬推出了一项新的服务"Google Trends",翻译成中文应该叫"谷歌趋势"。在Google的中国网志中是这样描述的:"(Google Trends)

Assessing Ebola-related web search behaviour: insights and implications from an analytical study of Google Trends-based query volumes

Background:The 2014 Ebola epidemic in West Africa has attracted public interest worldwide,leading to millions of Ebola-related Internet searches being performed during the period of the epidemic.This study aimed to evaluate and interpret Google search queries for terms related to the Ebola outbreak both at the global level and in all countries where primary cases of Ebola occurred.The study also endeavoured to look at the correlation between the number of overall and weekly web searches and the number of overall and weekly new cases of Ebola.Methods:Google Trends(GT)was used to explore Internet activity related to Ebola.The study period was from 29 December 2013 to 14 June 2015.Pearson’s correlation was performed to correlate Ebola-related relative search volumes(RSVs)with the number of weekly and overall Ebola cases.Multivariate regression was performed using Ebola-related RSV as a dependent variable,and the overall number of Ebola cases and the Human Development Index were used as predictor variables.Results:The greatest RSV was registered in the three West African countries mainly affected by the Ebola epidemic.The queries varied in the different countries.Both quantitative and qualitative differences between the affected African countries and other Western countries with primary cases were noted,in relation to the different flux volumes and different time courses.In the affected African countries,web query search volumes were mostly concentrated in the capital areas.However,in Western countries,web queries were uniformly distributed over the national territory.In terms of the three countries mainly affected by the Ebola epidemic,the correlation between the number of new weekly cases of Ebola and the weekly GT index varied from weak to moderate.The correlation between the number of Ebola cases registered in all countries during the study period and the GT index was very high.Conclusion:Google Trends showed a coarse-grained nature,strongly correlating with global epidemiological data,but was weaker at country level,as it was prone to distortions induced by unbalanced media coverage and the digital divide.Global and local health agencies could usefully exploit GT data to identify disease-related information needs and plan proper communication strategies,particularly in the case of health-threatening events.

Improving Google Flu Trends for COVID-19 estimates using Weibo posts

While incomplete non-medical data has been integrated into prediction models for epidemics,the accuracy and the generalizability of the data are difficult to guarantee.To comprehensively evaluate the ability and applicability of using social media data to predict the development of COVID-19,a new confirmed case prediction algorithm improving the Google Flu Trends algorithm is established,called Weibo COVID-19 Trends(WCT),based on the post dataset generated by all users in Wuhan on Sina Weibo.A genetic algorithm is designed to select the keyword set for filtering COVID-19 related posts.WCT can constantly outperform the highest average test score in the training set between daily new confirmed case counts and the prediction results.It remains to produce the best prediction results among other algorithms when the number of forecast days increases from one to eight days with the highest correlation score from 0.98(P<0.01)to 0.86(P<0.01)during all analysis period.Additionally,WCT effectively improves the Google Flu Trends algorithm's shortcoming of overestimating the epidemic peak value.This study offers a highly adaptive approach for feature engineering of third-party data in epidemic prediction,providing useful insights for the prediction of newly emerging infectious diseases at an early stage.

Prediction of COVID-19 Transmission in the United States Using Google Search Trends

Accurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health resources.Due to the exponential spread of the COVID-19 infection worldwide,several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the literature.To accelerate scientific and public health insights into the spread and impact of COVID-19,Google released the Google COVID-19 search trends symptoms open-access dataset.Our objective is to develop 7 and 14-day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related symptoms.Specifically,we propose a stacked long short-term memory(SLSTM)architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms dataset.Considering the SLSTM networks trained using historical data only as the base models,our base models for 7 and 14-day-ahead forecasting of COVID cases had the mean absolute percentage error(MAPE)values of 6.6%and 8.8%,respectively.On the other side,our proposed models had improved MAPE values of 3.2%and 5.6%,respectively.For 7 and 14-day-ahead forecasting of COVID-19 deaths,the MAPE values of the base models were 4.8%and 11.4%,while the improved MAPE values of our proposed models were 4.7%and 7.8%,respectively.We found that the Google search trends for“pneumonia,”“shortness of breath,”and“fever”are the most informative search trends for predicting COVID-19 transmission.We also found that the search trends for“hypoxia”and“fever”were the most informative trends for forecasting COVID-19 mortality.

Search trends in the treatment for benign prostatic hyperplasia:A twenty-year analysis

ObjectiveMinimally invasive treatments for benign prostatic hyperplasia (BPH) have seen an increase in usage in recent years. We aimed to determine what types of events may influence patient search habits related to surgical BPH treatments.MethodsGoogle Trends was used to determine the frequency of searches for different minimally invasive and prostatic ablative treatments for BPH in the United States. The procedures including transurethral resection of the prostate (TURP), Aquablation therapy (Aquablation), Greenlight laser therapy (Greenlight), transurethral needle ablation, transurethral microwave thermotherapy, Urolift (prostatic urethral lift [PUL]), Rezum, iTind, holmium laser enucleation of the prostate, simple prostatectomy, and prostatic artery embolization were compared.ResultsFrom January 1, 2004 to February 28, 2023, the number of internet search queries have increased for TURP, PUL, Rezum, prostatic artery embolization, and holmium laser enucleation of the prostate. There has been a slight decrease in searches for Greenlight, transurethral needle ablation, transurethral microwave thermotherapy, iTind, simple prostatectomy, and Aquablation.ConclusionDespite increased searches of alternatives, TURP remains the most searched BPH procedure. Additionally, search habits may be influenced by several factors including government approval, corporate acquisition, and marketing campaigns. It is important for physicians to understand the types of events that may cause patients to inquire about certain treatments for better quality health information and clinical visits.

The time‑varying causal relationship between the Bitcoin market and internet attention

The increasing attention on Bitcoin since 2013 prompts the issue of possible evidence for a causal relationship between the Bitcoin market and internet attention.Taking the Google search volume index as the measure of internet attention,time-varying Granger causality between the global Bitcoin market and internet attention is examined.Empirical results show a strong Granger causal relationship between internet attention and trading volume.Moreover,they indicate,beginning in early 2018,an even stronger impact of trading volume on internet attention,which is consistent with the rapid increase in Bitcoin users following the 2017 Bitcoin bubble.Although Bitcoin returns are found to strongly affect internet attention,internet attention only occasionally affects Bitcoin returns.Further investigation reveals that interactions between internet attention and returns can be amplified by extreme changes in prices,and internet attention is more likely to lead to returns during Bitcoin bubbles.These empirical findings shed light on cryptocurrency investor attention theory and imply trading strategy in Bitcoin markets.

China futures price forecasting based on online search and information transfer

The synchronicity effect between the financial market and online response for time-series forecasting is an important task with wide applications.This study combines data from the Baidu index(BDI),Google trends(GT),and transfer entropy(TE)to forecast a wide range of futures prices with a focus on China.A forecasting model based on a hybrid gray wolf optimizer(GWO),convolutional neural network(CNN),and long short-term memory(LSTM)is developed.First,Baidu and Google dual-platform search data were selected and constructed as Internetbased consumer price index(ICPI)using principal component analysis.Second,TE is used to quantify the information between online behavior and futures markets.Finally,the effective Internet-based consumer price index(ICPI)and TE are introduced into the GWO-CNN-LSTM model to forecast the daily prices of corn,soybean,polyvinyl chloride(PVC),egg,and rebar futures.The results show that the GWO-CNN-LSTM model has a significant improvement in predicting future prices.Internet-based CPI built on Baidu and Google platforms has a high degree of real-time performance and reduces the platform and language bias of the search data.Our proposed framework can provide predictive decision support for government leaders,market investors,and production activities.

The impact of COVID-19 pandemic on teeth grinding in the US and UK

Background:Preliminary evidence suggests that the burden of stress and anxiety may have considerably increased during the coronavirus disease 2019(COVID-19)pandemic.Since these two mental health-related factors are important causes of teeth grinding,we carried out an infodemiological analysis to define whether the burden of teeth grinding may have increased as a consequence of COVID-19.Methods:We conducted an electronic search in Google Trends,with the term“teeth grinding”,setting the geographical area to“US”or“UK”and the search period between July 2017 and July 2022.The weekly Google Trends score for“teeth grinding”was downloaded,and the difference in the volume Google searches for“teeth grinding”was compared between the pre-COVID-19 and COVID-19 periods in both countries.Results:The median value of weekly Google Trends score for“teeth grinding”was found to be significantly increased after emergence of the COVID-19 pandemic both in the UK(57 with interquartile range[IQR]51-64 vs.48 with IQR 42-53;+19%and P<0.001)and the US(78 with IQR 73-83 vs.70 with IQR 66-74;+11%and P<0.001),compared to the homologous period before.Conclusion:The results of this infodemiological analysis reveal that the volume of Web searches for“teeth grinding”in both the UK and US has considerably increased after emergence of the COVID-19 pandemic,thus probably reflecting an increased burden of this condition in the general population.

Time-varying nexus and causality in the quantile between Google investor sentiment and cryptocurrency returns

Understanding the interplay between investor sentiment and cryptocurrency returns has become a critical area of research.Indeed,this study aims to uncover the role of Google investor sentiment on cryptocurrency returns(including Bitcoin,Litecoin,Ethereum,and Tether),especially during the 2017-18 bubble(January 01,2017,to December 31,2018)and the COVID-19 pandemic(January 01,2020,to March 15,2022).To achieve this,we use two techniques:quantile causality and wavelet coherence.First,the quantile causality test revealed that investors’optimistic sentiments have notably higher cryptocurrency returns,whereas pessimistic sentiments have significantly opposite effects.Moreover,the wavelet coherence analysis shows that co-movement between investor sentiment and Tether cannot be considered significant.This result supports the role of Tether as a stablecoin in portfolio diversification strategies.In fact,the findings will help investors improve the accuracy of cryptocurrency return forecasts in times of stressful events and pave the way for enhanced decision-making utility.

A demonstration project of Global Alliance against Chronic Respiratory Diseases:Prediction of interactions between air pollution and allergen exposure—the Mobile Airways Sentinel NetworK-Impact of air POLLution on Asthma and Rhinitis approach

This review analyzes the state and recent progress in the field of information support for pollen allergy sufferers.For decades,information available for the patients and allergologists consisted of pollen counts,which are vital but insufficient.New technology paves the way to substantial increase in amount and diversity of the data.This paper reviews old and newly suggested methods to predict pollen and air pollutant concentrations in the air and proposes an allergy risk concept,which combines the pollen and pollution information and transforms it into a qualitative risk index.This new index is available in an app(Mobile Airways Sentinel NetworK-air)that was developed in the frame of the European Union grant Impact of Air POLLution on sleep,Asthma and Rhinitis(a project of European Institute of Innovation and Technology-Health).On-going transformation of the pollen allergy information support is based on new technological solutions for pollen and air quality monitoring and predictions.The new information-technology and artificial-intelligence-based solutions help to convert this information into easy-to-use services for both medical practitioners and allergy sufferers.

Chinese Online Unemployment-Related Searches and Macroeconomic Indicators

Official monthly unemployment data is unavailable in China, while intense public interest in unemployment requires timely and accurate information. Using data on web queries from lead search engines in China, Baidu and Google, I build two indices measuring intensity of online unemployment-related searches. The unemployment-related search indices identify a structural break in the time series between October and November 2008, which corresponds to a turning point indicated by some macroeconomic indicators. The unemployment- related search indices are proven to have significant correlation with Purchasing Managers＇ Employment Indices and a set of macroeconomic indicators that are closely related to changes in unemployment in China. The results of Granger causality analysis show that the unemployment-related search indices can improve predictions of the c indicators. It suggests that unemploy- ment-related searches can potentially provide valuable, timely, and low-cost information for macroeconomic monitoring.

Citizen science reporting indicates geographic and phenotypic drivers of road use and mortality in a threatened rattlesnake

Roads may influence the selection of phenotypic traits of wildlife.In particular,the likelihood of vehicle collisions with wildlife may vary depending on body coloration in contrast to the road,which may be exaggerated by cultural attitudes toward the species.The timber rattlesnake Crotalus horridus is a threatened species that varies widely in coloration,and their color pattern could influence thermoregulatory use of roads and visibility to motorists.Moreover,better-camouflaged snakes may have higher road mortality in areas where environmental interest is lower and,perhaps,negative attitudes toward wildlife are more prevalent.We used citizen scientist observations of timber rattlesnakes from iNaturalist and categorized for each rattlesnake the surface they were on,its color pattern,and whether they were alive.We combined iNaturalist data with Google Trends data to characterize regional variation in environmental interest.We discovered that lighter-colored snakes were more likely to be found on roads,as were snakes further south,west,and on warmer days.Once on a road,coloration did not influence survival regardless of road type or environmental interest.However,snakes on asphalt roads or on southern roads were more likely to be found dead.The higher likelihood of lighter-colored snakes being found on roads suggests that they are at a greater overall risk of road death,potentially selecting for darker coloration.Citizen scientist behavior may at least partly underlie the influence of latitude on the results,however,and further work in the application of citizen science data to such research questions is warranted.

The effects of the China–US trade war during 2018–2019 on the Chinese economy:an initial assessment

The China–US trade war during 2018–2019 has attracted attentions from academics,policy makers,businesses and investors around the world.Unlike previous researches which are mainly based on hypothetical scenarios,this study looks at the real effects of the China–US trade war on the Chinese economy.Based on either weekly or monthly data during January 2018–December 2019 including creatively using the Google Trends data to measure the severity of the trade war,this study examines the effects of the China–US trade war on Chinese Renminbi,China–US bilateral trade and stock markets.