Topic Mapping: a Tool for Finding the Meaning of Internet Search Queries

2006 ◽  
Author(s):  
D. Tikk ◽  
Z.T. Kardkovacs ◽  
Z. Bansaghi
Keyword(s):  
Internet Search ◽  
Search Queries

scholarly journals Using Internet Search Queries to Assess Public Awareness of the Healthy Cities Approach: A Case Study in Shenzhen, China

2021 ◽  
Vol 18 (8) ◽  
pp. 4264
Author(s):  
Jun Yang ◽  
Yutong Zhang ◽  
Yixiong Xiao ◽  
Shaoqing Shen ◽  
Mo Su ◽  
...  
Keyword(s):  
Public Awareness ◽  
Healthy Cities ◽  
Internet Search ◽  
Health Concerns ◽  
Search Queries ◽  
The Public ◽  
Health Factors ◽  
Data Source ◽  
The City

Cities around the globe are embracing the Healthy Cities approach to address urban health challenges. Public awareness is vital for successfully deploying this approach but is rarely assessed. In this study, we used internet search queries to evaluate the public awareness of the Healthy Cities approach applied in Shenzhen, China. The overall situation at the city level and the intercity variations were both analyzed. Additionally, we explored the factors that might affect the internet search queries of the Healthy Cities approach. Our results showed that the public awareness of the approach in Shenzhen was low. There was a high intercity heterogeneity in terms of interest in the various components of the Healthy Cities approach. However, we did not find a significant effect of the selected demographic, environmental, and health factors on the search queries. Based on our findings, we recommend that the city raise public awareness of healthy cities and take actions tailored to health concerns in different city zones. Our study showed that internet search queries can be a valuable data source for assessing the public awareness of the Healthy Cities approach.

scholarly journals Predicting taxi demand hotspots using automated Internet Search Queries

2019 ◽  
Vol 102 ◽  
pp. 73-86 ◽  
Author(s):  
Ioulia Markou ◽  
Kevin Kaiser ◽  
Francisco C. Pereira
Keyword(s):  
Internet Search ◽  
Search Queries

scholarly journals How popular is waterpipe tobacco smoking? Findings from internet search queries

2014 ◽  
Vol 24 (5) ◽  
pp. 509-513 ◽  
Author(s):  
Ramzi G Salloum ◽  
Amira Osman ◽  
Wasim Maziak ◽  
James F Thrasher
Keyword(s):  
Tobacco Smoking ◽  
Internet Search ◽  
Search Queries ◽  
Waterpipe Tobacco Smoking

Predicting the Number of Suicides in Japan: A Vector Autoregression Time Series Model Using Internet Search Queries (Preprint)

2021 ◽  
Author(s):  
Kazuya Taira ◽  
Rikuya Hosokawa ◽  
Tomoya Itatani ◽  
Sumio Fujita
Keyword(s):  
Granger Causality ◽  
Vector Autoregression ◽  
Unit Roots ◽  
Granger Causality Test ◽  
Lagrangian Multiplier ◽  
Internet Search ◽  
Causality Test ◽  
Search Queries ◽  
Original Series ◽  
Lm Test

BACKGROUND The number of suicides in Japan increased during the COVID-19 pandemic. Predicting the number of suicides is critical to take timely preventive measures. OBJECTIVE In this study, we examine whether the number and characteristics of suicides can be predicted based on the Internet search behavior and the search queries. METHODS The monthly number of suicides by gender, collected and published by the National Police Agency, was used as an outcome variable. The number of searches by gender on the queries associated with "suicide" on "Yahoo Search" from January 2016 to December 2020 was used as a predictive variable. The following five phrases highly relevant to "suicide" were searched before searching for the keyword "suicide," and extracted and used for analyses: "abuse," "work, don’t want to go," "company, want to quit," "divorce," and "no money." The Augmented Dickey–Fuller and Johansen's tests were performed for the original series and to verify the existence of unit roots and cointegration for each variable, respectively. The vector autoregression model was applied to predict the number of suicides. The Breusch–Godfrey Lagrangian multiplier (BG-LM) test, autoregressive conditional heteroskedasticity Lagrangian multiplier (ARCH-LM) test, and Jarque–Bera (JB) test were employed to confirm model convergence. In addition, a Granger causality test was performed for each predictive variable. RESULTS In the original series, unit roots were found in the trend model, whereas in the first-order difference series, both men (minimum tau 3: −9.24, max tau 3: −5.38) and women (minimum tau 3: −9.24, max tau 3: −5.38) had no unit roots for all variables. In Johansen's test, a cointegration relationship was observed among several variables. The queries used in the converged models were "divorce" for men (BG-LM test: p= 0.55; ARCH-LM test: p= 0.63; JB test: p= 0.66) and "no money" for women (BG-LM test: p = 0.17; ARCH-LM test: p = 0.15; JB test: p= 0.10). In the Granger causality test for each variable, "divorce" was significant for both men (F= 3.29, p = 0.041) and women (F = 3.23, p = 0.044). ¬ CONCLUSIONS The number of suicides can be predicted by the search queries related to the keyword "suicide." Previous studies have reported that financial poverty and divorce are associated with suicide. The results of this study, in which search queries on "no money" and "divorce" predict suicide, support the findings of previous studies. Further research on the economic poverty of women and those with complex problems is necessary.

scholarly journals Can internet search queries be used for dengue fever surveillance in China?

2017 ◽  
Vol 63 ◽  
pp. 74-76 ◽  
Author(s):  
Pi Guo ◽  
Li Wang ◽  
Yanhong Zhang ◽  
Ganfeng Luo ◽  
Yanting Zhang ◽  
...  
Keyword(s):  
Dengue Fever ◽  
Internet Search ◽  
Search Queries

scholarly journals Forecasting influenza epidemics by integrating internet search queries and traditional surveillance data with the support vector machine regression model in Liaoning, from 2011 to 2015

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5134 ◽  
Author(s):  
Feng Liang ◽  
Peng Guan ◽  
Wei Wu ◽  
Desheng Huang
Keyword(s):  
Support Vector Machine ◽  
Regression Model ◽  
Search Engine ◽  
Influenza Surveillance ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Internet Search ◽  
Search Query ◽  
Search Queries

Background Influenza epidemics pose significant social and economic challenges in China. Internet search query data have been identified as a valuable source for the detection of emerging influenza epidemics. However, the selection of the search queries and the adoption of prediction methods are crucial challenges when it comes to improving predictions. The purpose of this study was to explore the application of the Support Vector Machine (SVM) regression model in merging search engine query data and traditional influenza data. Methods The official monthly reported number of influenza cases in Liaoning province in China was acquired from the China National Scientific Data Center for Public Health from January 2011 to December 2015. Based on Baidu Index, a publicly available search engine database, search queries potentially related to influenza over the corresponding period were identified. An SVM regression model was built to be used for predictions, and the choice of three parameters (C, γ, ε) in the SVM regression model was determined by leave-one-out cross-validation (LOOCV) during the model construction process. The model’s performance was evaluated by the evaluation metrics including Root Mean Square Error, Root Mean Square Percentage Error and Mean Absolute Percentage Error. Results In total, 17 search queries related to influenza were generated through the initial query selection approach and were adopted to construct the SVM regression model, including nine queries in the same month, three queries at a lag of one month, one query at a lag of two months and four queries at a lag of three months. The SVM model performed well when with the parameters (C = 2, γ = 0.005, ɛ = 0.0001), based on the ensemble data integrating the influenza surveillance data and Baidu search query data. Conclusions The results demonstrated the feasibility of using internet search engine query data as the complementary data source for influenza surveillance and the efficiency of SVM regression model in tracking the influenza epidemics in Liaoning.

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