Baidu Search Engine Research Articles

Effects of the Ambient Fine Particulate Matter on Public Awareness of Lung Cancer Risk in China: Evidence from the Internet-Based Big Data Platform.

BackgroundIn October 2013, the International Agency for Research on Cancer classified the particulate matter from outdoor air pollution as a group 1 carcinogen and declared that particulate matter can cause lung cancer. Fine particular matter (PM2.5) pollution is becoming a serious public health concern in urban areas of China. It is essential to emphasize the importance of the public’s awareness and knowledge of modifiable risk factors of lung cancer for prevention.ObjectiveThe objective of our study was to explore the public’s awareness of the association of PM2.5 with lung cancer risk in China by analyzing the relationship between the daily PM2.5 concentration and searches for the term “lung cancer” on an Internet big data platform, Baidu.MethodsWe collected daily PM2.5 concentration data and daily Baidu Index data in 31 Chinese capital cities from January 1, 2014 to December 31, 2016. We used Spearman correlation analysis to explore correlations between the daily Baidu Index for lung cancer searches and the daily average PM2.5 concentration. Granger causality test was used to analyze the causal relationship between the 2 time-series variables.ResultsIn 23 of the 31 cities, the pairwise correlation coefficients (Spearman rho) between the daily Baidu Index for lung cancer searches and the daily average PM2.5 concentration were positive and statistically significant (P<.05). However, the correlation between the daily Baidu Index for lung cancer searches and the daily average PM2.5 concentration was poor (all r2s<.1). Results of Granger causality testing illustrated that there was no unidirectional causality from the daily PM2.5 concentration to the daily Baidu Index for lung cancer searches, which was statistically significant at the 5% level for each city.ConclusionsThe daily average PM2.5 concentration had a weak positive impact on the daily search interest for lung cancer on the Baidu search engine. Well-designed awareness campaigns are needed to enhance the general public’s awareness of the association of PM2.5 with lung cancer risk, to lead the public to seek more information about PM2.5 and its hazards, and to cope with their environment and its risks appropriately.

Identifying Potential Norovirus Epidemics in China via Internet Surveillance.

BackgroundNorovirus is a common virus that causes acute gastroenteritis worldwide, but a monitoring system for norovirus is unavailable in China.ObjectiveWe aimed to identify norovirus epidemics through Internet surveillance and construct an appropriate model to predict potential norovirus infections.MethodsThe norovirus-related data of a selected outbreak in Jiaxing Municipality, Zhejiang Province of China, in 2014 were collected from immediate epidemiological investigation, and the Internet search volume, as indicated by the Baidu Index, was acquired from the Baidu search engine. All correlated search keywords in relation to norovirus were captured, screened, and composited to establish the composite Baidu Index at different time lags by Spearman rank correlation. The optimal model was chosen and possibly predicted maps in Zhejiang Province were presented by ArcGIS software.ResultsThe combination of two vital keywords at a time lag of 1 day was ultimately identified as optimal (ρ=.924, P<.001). The exponential curve model was constructed to fit the trend of this epidemic, suggesting that a one-unit increase in the mean composite Baidu Index contributed to an increase of norovirus infections by 2.15 times during the outbreak. In addition to Jiaxing Municipality, Hangzhou Municipality might have had some potential epidemics in the study time from the predicted model.ConclusionsAlthough there are limitations with early warning and unavoidable biases, Internet surveillance may be still useful for the monitoring of norovirus epidemics when a monitoring system is unavailable.

Monitoring seasonal influenza epidemics by using internet search data with an ensemble penalized regression model

Seasonal influenza epidemics cause serious public health problems in China. Search queries-based surveillance was recently proposed to complement traditional monitoring approaches of influenza epidemics. However, developing robust techniques of search query selection and enhancing predictability for influenza epidemics remains a challenge. This study aimed to develop a novel ensemble framework to improve penalized regression models for detecting influenza epidemics by using Baidu search engine query data from China. The ensemble framework applied a combination of bootstrap aggregating (bagging) and rank aggregation method to optimize penalized regression models. Different algorithms including lasso, ridge, elastic net and the algorithms in the proposed ensemble framework were compared by using Baidu search engine queries. Most of the selected search terms captured the peaks and troughs of the time series curves of influenza cases. The predictability of the conventional penalized regression models were improved by the proposed ensemble framework. The elastic net regression model outperformed the compared models, with the minimum prediction errors. We established a Baidu search engine queries-based surveillance model for monitoring influenza epidemics, and the proposed model provides a useful tool to support the public health response to influenza and other infectious diseases.

Investigation on events of bus on fire in 6 years in the mainland of China

Objective: To retrospectively analyze the characteristics of events of bus on fire in 6 years in the mainland of China. Methods: Events of bus on fire happened between January 2009 and December 2014 were retrieved through Baidu search engine, Chinese Journals Full-text Database, and PubMed database in the search strategy with " bus" and " fire" or " arson" as keywords combined with the name of provinces, autonomous regions, and municipalities of the mainland of China. The occurrence time, region, cause of fire, casualties of each event were recorded, and the correlative analysis was conducted. Data were processed with Microsoft Excel software. Results: Totally 287 events of bus on fire were retrieved, among which 49 events happened in 2009, 36 events happened in 2010, 35 events happened in 2011, 37 events happened in 2012, and respectively 65 events happened in 2013 and 2014. The events of bus on fire most frequently happened in June and July, respectively 49 and 39 events. Among the distribution of occurrence regions of events of bus on fire, there were 78 events (27.18%) in east China, 52 events (18.12%) in northeast China, 41 events (14.29%) both in north China and south China. Among the causes of events of bus on fire, spontaneous combustion of bus ranked in the first (267 events, accounting for 93.03%), followed by arson (13 events, accounting for 4.53%). Among the 13 events of bus on fire caused by arson, 7 events happened between 16: 00 and 20: 00, and 3 events happened between 8: 00 and 10: 00. Totally 27 events of bus on fire (9.41%) were with casualties, among which 13 events (48.15%) were caused by spontaneous combustion of bus, 10 events (37.04%) were caused by arson, and 4 events (14.81%) were caused by traffic accidents. Arson caused the most severe casualties (at least 88 deaths and 287 injuries), followed by spontaneous combustion of bus (at least 35 deaths and 140 injuries) and traffic accidents (at least 9 deaths and 20 injuries). Conclusions: Events of bus on fire happened more frequently in recent years in the mainland of China, and the frequencies were much higher especially in June and July. Most events were caused by spontaneous combustion of bus, followed by arson. Most of the events of bus on fire caused by arson happened in the morning and evening rush hours of urban traffic, and althouth the occurrence rate was not high, the casualties were most severe.

Impact of Predicting Health Care Utilization Via Web Search Behavior: A Data-Driven Analysis.

BackgroundBy recent estimates, the steady rise in health care costs has deprived more than 45 million Americans of health care services and has encouraged health care providers to better understand the key drivers of health care utilization from a population health management perspective. Prior studies suggest the feasibility of mining population-level patterns of health care resource utilization from observational analysis of Internet search logs; however, the utility of the endeavor to the various stakeholders in a health ecosystem remains unclear.ObjectiveThe aim was to carry out a closed-loop evaluation of the utility of health care use predictions using the conversion rates of advertisements that were displayed to the predicted future utilizers as a surrogate. The statistical models to predict the probability of user’s future visit to a medical facility were built using effective predictors of health care resource utilization, extracted from a deidentified dataset of geotagged mobile Internet search logs representing searches made by users of the Baidu search engine between March 2015 and May 2015.MethodsWe inferred presence within the geofence of a medical facility from location and duration information from users’ search logs and putatively assigned medical facility visit labels to qualifying search logs. We constructed a matrix of general, semantic, and location-based features from search logs of users that had 42 or more search days preceding a medical facility visit as well as from search logs of users that had no medical visits and trained statistical learners for predicting future medical visits. We then carried out a closed-loop evaluation of the utility of health care use predictions using the show conversion rates of advertisements displayed to the predicted future utilizers. In the context of behaviorally targeted advertising, wherein health care providers are interested in minimizing their cost per conversion, the association between show conversion rate and predicted utilization score, served as a surrogate measure of the model’s utility.ResultsWe obtained the highest area under the curve (0.796) in medical visit prediction with our random forests model and daywise features. Ablating feature categories one at a time showed that the model performance worsened the most when location features were dropped. An online evaluation in which advertisements were served to users who had a high predicted probability of a future medical visit showed a 3.96% increase in the show conversion rate.ConclusionsResults from our experiments done in a research setting suggest that it is possible to accurately predict future patient visits from geotagged mobile search logs. Results from the offline and online experiments on the utility of health utilization predictions suggest that such prediction can have utility for health care providers.

Baidu, Weibo and Renren: the global political economy of social media in China

The task of this work is to conduct a global political-economic analysis of China's major social media platforms in the context of transformations of the Chinese economy. It analyses Chinese social media's commodity and capital form. It compares the political economy of Baidu (search engine), Weibo (microblog) and Renren (social networking site) to the political economy of the US platforms Google (search engine), Twitter (microblog) and Facebook (social networking site) in order to analyse differences and commonalities. The comparative analysis focuses on aspects such as profits, the role of advertising, the boards of directors, shareholders, financial market values, terms of use and usage policies. The analysis is framed by the question to which extent China has a capitalist or socialist economy.

Correlation between reported human infection with avian influenza A H7N9 virus and cyber user awareness: what can we learn from digital epidemiology?

Data on the topic of novel avian influenza A (H7N9) were collected based on the web analysis tool 'Baidu Index', a major Chinese search engine. We found a positive correlation between the volume of H7N9-related 'cyber user awareness' and the epidemic situation during the H7N9 outbreak in China (r=0.98, p<0.01, cumulative; r=0.43, p=0.018, daily) except in the early stage; the ranks of H7N9-related topics changed at different epidemic stages. This study may improve our understanding of the role of web-based media in infectious disease surveillance in China.

Web News Oriented Event Multi-Elements Retrieval

To meet the demand of effectively acquiring event information,a method of Web news-oriented event multi-elements retrieval is studied through analyzing characteristics of Web news and event multi-elements retrieval process.Firstly,a model of Web news-oriented event multi-elements retrieval is proposed.Secondly,event multi-elements query terms are formally defined by using the BNF(Backus-Naur form).Finally,incorporating the importance of event action element,Web news title and the distance between event terms and constrained terms,a method of computing the relevance between query terms and the document is proposed.Sixteen event query topics are created to implement the experiments.With the proposed method,this paper evaluates the index P@n based on the Baidu search engine,getting average P@10 of 0.85 and average P@20 of 0.83.This paper also evaluates the index F-measure through manually labeling the corpus with same method,obtaining average F-measure of 0.74.The results show that the proposed method offers more effective performances.

The foxconn suicides and their media prominence: is the werther effect applicable in china?

BackgroundMedia reporting of suicide and its relationship with actual suicide has rarely been investigated in Mainland China. The "Foxconn suicides" is a description referring to a string of suicides/attempts during 2010, all of which were related to a giant electrical manufacturing company, Foxconn. This study aimed to examine the clustering and copycat effects of the Foxconn suicides, and to investigate temporal patterns in how they were reported by the media in Mainland China, Hong Kong (HK), and Taiwan (TW).MethodsRelevant articles were collected from representative newspapers published in three big cities in Mainland China (Beijing (BJ), Shenzhen (SZ), and Guangzhou (GZ)), HK, and TW, together with searching intensity data on the topic conducted using the Baidu search engine in Mainland China. The temporal clustering effects of the Foxconn suicides and their media prominence were assessed using the Kolmogorov-Smirnov test. The media reports of the Foxconn suicides' temporal patterns were explored using a nonparametric curve estimation method (that is, the local linear method). The potential mutual interactions between the Foxconn suicides and their media prominence were also examined, using logistic and Poisson regression methods.ResultsThe results support a temporal clustering effect for the Foxconn suicides. The BJ-based newspapers' reporting and the occurrence of a Foxconn suicide/attempt are each found to be associated with an elevated chance of a further Foxconn suicide 3 days later. The occurrence of a Foxconn suicide also immediately influenced the intensity of both Baidu searching and newspaper reporting. Regional diversity in suicide reporting tempo-patterns within Mainland China, and similarities between HK and TW, are also demonstrated.ConclusionsThe Foxconn suicides were temporally clustered. Their occurrences were influenced by the reporting of BJ-based newspapers, and contagion within the company itself. Further suicide research and prevention work in China should consider its special media environment.

Notifiable infectious disease surveillance with data collected by search engine

Notifiable infectious diseases are a major public health concern in China, causing about five million illnesses and twelve thousand deaths every year. Early detection of disease activity, when followed by a rapid response, can reduce both social and medical impact of the disease. We aim to improve early detection by monitoring health-seeking behavior and disease-related news over the Internet. Specifically, we counted unique search queries submitted to the Baidu search engine in 2008 that contained disease-related search terms. Meanwhile we counted the news articles aggregated by Baidu’s robot programs that contained disease-related keywords. We found that the search frequency data and the news count data both have distinct temporal association with disease activity. We adopted a linear model and used searches and news with 1–200-day lead time as explanatory variables to predict the number of infections and deaths attributable to four notifiable infectious diseases, i.e., scarlet fever, dysentery, AIDS, and tuberculosis. With the search frequency data and news count data, our approach can quantitatively estimate up-to-date epidemic trends 10–40 days ahead of the release of Chinese Centers for Disease Control and Prevention (Chinese CDC) reports. This approach may provide an additional tool for notifiable infectious disease surveillance.

On the Regulation of Advertising Law on the Bid Ranking of Search Engine

In the virtual network world in information age, the legal authority is being faced with constant challenges. While operation model of bid ranking of search engine not only creates enormous economic benefit, but also makes the authenticity and fairness of information searching be increasingly questioned, which requires to be regulated by law objectively. According to the China's existing laws, the application of law is still blank in this regard. Whether the bid ranking of search engine should be regulated and how the legal relationships should be regulated by Advertising Law still remain open. Taking the Baidu search engine for an example, the authors will explore relevant legal regulations and put forward some proposal to to regulate the bid ranking under the perspective of Advertising Law.

Case Comment: Yahoo and Baidu Search Engine Liability in China

In January, 2007, Universal Music Group, Warner Music Group, EMI Music Group and Sony BMG Music Entertainment Inc and others jointly sued Beijing Alibaba Information Technology Co. Ltd., the owner of 'Yahoo! China', for infringement of copyright in two hundred and twenty nine audiovisual works.The evidence before the Beijing Second Intermediate Court established that the search engine, 'Yahoo! China' provided hyperlinks to unauthorized copies of the plaintiff's music in its search results. The plaintiffs served 'Take-down Notices' on the defendant which provided sample URL addresses of some but not all infringing music and requested the defendant to delete all relevant infringing links. On receipt 'Yahoo! China' canceled the particular hyperlink addresses provided in the plaintiff's samples but this did not include all the infringing links related to the music involved.