• Journal of Dental Anesthesia and Pain Medicine
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• 제21권2호
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• pp.113-118
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• 2021

Background: Consumers and patients in the last two decades have increasingly turned to various internet search engines including Google for information. Google Trends records searches done using the Google search engine. Google Trends is free and provides data on search terms and related queries. One recent study found a large public interest in "dental anesthesia". In this paper, we further explore this interest in "dental anesthesia" and assess if any patterns emerge. Methods: In this study, Google Trends and the search term "dental pain" was used to record the consumer's interest over a five-year period. Additionally, using the search term "Dental anesthesia," a top ten related query list was generated. Queries are grouped into two sections, a "top" category and a "rising" category. We then added additional search term such as: wisdom tooth anesthesia, wisdom tooth general anesthesia, dental anesthetics, local anesthetic, dental numbing, anesthesia dentist, and dental pain. From the related queries generated from each search term, repeated themes were grouped together and ranked according to the total sum of their relative search frequency (RSF) values. Results: Over the five-year time period, Google Trends data show that there was a 1.5% increase in the search term "dental pain". Results of the related queries for dental anesthesia show that there seems to be a large public interest in how long local anesthetics last (Total RSF = 231) - even more so than potential side effects or toxicities (Total RSF = 83). Conclusion: Based on these results it is recommended that clinicians clearly advice their patients on how long local anesthetics last to better manage patient expectations.

• 경영과학
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• 제32권3호
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• pp.91-103
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• 2015

This study recognizes that there is a correlation between the movement of the financial market and the sentimental changes of the public participating directly or indirectly in the market, and applies the relationship to investment strategies for stock market. The concerns that market participants have about the economy can be transformed to the search terms that internet users query on search engines, and search volume of a specific term over time can be understood as the economic trend of big data. Under the hypothesis that the time when the economic concerns start increasing precedes the decline in the stock market price and vice versa, this study proposes three investment strategies using casuality between price of domestic stock market and search volume from Naver trends, and verifies the hypothesis. The computational results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior in domestic stock market.

• 동의신경정신과학회지
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• 제34권3호
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• pp.235-245
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• 2023

Objectives: The aim of this study was to analyze public and researcher interests in suicide and related illnesses and acupuncture and acupressure treatment using Google Trends and some electronic databases. Methods: Search results for keywords "suicide," "acupuncture," "acupressure," and several illnesses related to suicide were analyzed in Google Trends from January 2004 to June 2023. Illnesses included anxiety, depression (including major depressive disorder), schizophrenia, bipolar disorder, post- traumatic stress disorder (PTSD), eating disorder (including anorexia nervosa and bulimia nervosa), substance use disorder, autism spectrum disorder, personality disorder (including borderline person- ality disorder), and chronic pain. Search results were extracted using relative search volume (RSV) scores between 0 and 100. Search terms were also searched in online databases, including PubMed, CNKI, and OASIS, to estimate the number of related studies, and descriptive analysis was conducted. Results: Google Trends analysis showed a strong positive correlation between the RSVs of "suicide and depression," "acupuncture and chronic pain," and "acupressure and PTSD." The electronic database search results produced numerous studies published on "suicide and depression," "acupuncture and depression," and "acupressure and anxiety." High interest in "suicide and depression," "acupuncture and chronic pain," and "acupressure and anxiety" was seen among the public and researchers. Interest in "suicide and chronic pain," "acupuncture and eating disorder," and "acupressure and PTSD" was higher in the public than among researchers, while "anxiety and suicide" and "anxiety and acu- puncture" showed opposite trends. Conclusions: The results of this research enable an understanding of public and researcher interest in suicide, acupuncture, acupressure, and suicide-related illnesses. The results also provide a basis for fu- ture research and examining public health implications in Korean medicine.

• Communications for Statistical Applications and Methods
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• 제29권1호
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• pp.41-51
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• 2022

We forecast the US oil consumption level taking advantage of google trends. The google trends are the search volumes of the specific search terms that people search on google. We focus on whether proper selection of google trend terms leads to an improvement in forecast performance for oil consumption. As the forecast models, we consider the least absolute shrinkage and selection operator (LASSO) regression and the structured regularization method for large vector autoregressive (VAR-L) model of Nicholson et al. (2017), which select automatically the google trend terms and the lags of the predictors. An out-of-sample forecast comparison reveals that reducing the high dimensional google trend data set to a low-dimensional data set by the LASSO and the VAR-L models produces better forecast performance for oil consumption compared to the frequently-used forecast models such as the autoregressive model, the autoregressive distributed lag model and the vector error correction model.

• Journal of Oral Medicine and Pain
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• 제48권4호
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• pp.137-143
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• 2023

Purpose: To assess Google Trends (GT) search behavior regarding orofacial pain (OFP) and headaches. Methods: GT scores for OFP and headache specialists between February 2013 and December 2022 were analyzed. Statistical tests such as Poisson regression analyses, mean differences, and Cohen's D were used to assess the score change over time. Results: The top three search words for OFP specialists were "temporomandibular joint (TMJ) specialist," "TMJ doctor," and "TMJ dentist," whereas the top three search words for headache specialists were "Headache specialist," "Headache doctor," and "Migraine specialist." Here, TMJ is temporomandibular joint. The GT scores for OFP specialists increased significantly (p<0.05) for all years except 2017, with the highest mean difference in 2020. The scores for headache specialists showed similar trends but gradually. Conclusions: The interest in OFP and headache specialists expressed by Google searches has increased over the years. More awareness is needed regarding the OFP scope of practice, and the use of GT may serve as an indicator.

• 치위생과학회지
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• 제21권2호
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• pp.104-110
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• 2021

Background: Regardless of countries, the myth that rain makes the body ache has been worded in various forms, and a number of studies have been reported to investigate this. However, these studies, which depended on the patient's experience or memory, had obvious limitations. Google Trends is a big data analysis service based on search terms and viewing videos provided by Google LLC, and attempts to use it in various fields are continuing. In this study, we endeavored to introduce the 'value as a research tool' of the Google Trends, that has emerged along with technological advancements, through research on 'whether toothaches really occur frequently on rainy days'. Methods: Keywords were selected as objectively as possible by applying web crawling and text mining techniques, and the keyword "bi" meaning rain in Korean was added to verify the reliability of Google Trends data. The correlation was statistically analyzed using precipitation and temperature data provided by the Korea Meteorological Agency and daily search volume data provided by Google Trends. Results: Keywords "chi-gwa", "chi-tong", and "chung-chi" were selected, which in Korean mean 'dental clinic', 'toothache', and 'tooth decay' respectively. A significant correlation was found between the amount of precipitation and the search volume of tooth decay. No correlation was found between precipitation and other keywords or other combinations. It was natural that a very significant correlation was found between the amount of precipitation, temperature, and the search volume of "bi". Conclusion: Rain seems to actually be a cause of toothache, and if objective keyword selection is premised, Google Trends is considered to be very useful as a research tool in the future.

• 한국정보시스템학회지:정보시스템연구
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• 제26권3호
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• pp.171-185
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• 2017

Purpose Recently, there has been an increase in attempts to analyze social phenomena, consumption trends, and consumption behavior through a vast amount of customer data such as web search traffic information and social buzz information in various fields such as flu prediction and real estate price prediction. Internet portal service providers such as google and naver are disclosing web search traffic information of online users as services such as google trends and naver trends. Academic and industry are paying attention to research on information search behavior and utilization of online users based on the web search traffic information. Although there are many studies predicting social phenomena, consumption trends, political polls, etc. based on web search traffic information, it is hard to find the research to explain and predict tourism demand and establish tourism policy using it. In this study, we try to use web search traffic information to explain the tourism demand for major cities in Gangwon-do, the representative tourist area in Korea, and to develop a nowcasting model for the demand. Design/methodology/approach In the first step, the literature review on travel demand and web search traffic was conducted in parallel in two directions. In the second stage, we conducted a qualitative research to confirm the information retrieval behavior of the traveler. In the next step, we extracted the representative tourist cities of Gangwon-do and confirmed which keywords were used for the search. In the fourth step, we collected tourist demand data to be used as a dependent variable and collected web search traffic information of each keyword to be used as an independent variable. In the fifth step, we set up a time series benchmark model, and added the web search traffic information to this model to confirm whether the prediction model improved. In the last stage, we analyze the prediction models that are finally selected as optimal and confirm whether the influence of the keywords on the prediction of travel demand. Findings This study has developed a tourism demand forecasting model of Gangwon-do, a representative tourist destination in Korea, by expanding and applying web search traffic information to tourism demand forecasting. We compared the existing time series model with the benchmarking model and confirmed the superiority of the proposed model. In addition, this study also confirms that web search traffic information has a positive correlation with travel demand and precedes it by one or two months, thereby asserting its suitability as a prediction model. Furthermore, by deriving search keywords that have a significant effect on tourism demand forecast for each city, representative characteristics of each region can be selected.

• 한국전자거래학회지
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• 제20권2호
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• pp.1-14
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• 2015

본 논문에서는 인터넷 검색 추세와 주식시장 사이에 어떤 관계가 있는지를 알아보고자 한다. 관심 기업의 정보를 얻기 위하여 투자자가 인터넷 검색엔진을 활용하고 이것이 실제 투자로 이어질 수 있다는 가정에서, 기업에 대한 검색량의 변화가 해당 기업의 주가 및 거래량 변동과 어떤 관계성이 있는지를 실제 데이터를 통해 분석하였다. 검색량의 변화를 기초로 한 검색트렌드 투자전략을 대기업 그룹과 중소기업 그룹에 적용하여, 두 그룹의 수익률 등락과 주식거래량에 대한 상관관계를 분석하였다. 7년(2007년~2013년)간의 데이터를 기초로 KOSPI와 KOSDAQ 모두에서 검색트렌드 투자전략이 시장의 평균 수익률 이상을 실현하고, 대기업보다는 중소기업에서 더 투자효과가 높다는 결과를 얻었다. 검색량과 주식거래량의 관계 또한 대기업보다는 중소기업이 더 영향을 받는다는 것을 알 수 있었다.

• Healthcare Informatics Research
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• 제24권4호
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• pp.300-308
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• 2018

Objectives: Prompt detection is a cornerstone in the control and prevention of infectious diseases. The Integrated Disease Surveillance Project of India identifies outbreaks, but it does not exactly predict outbreaks. This study was conducted to assess temporal correlation between Google Trends and Integrated Disease Surveillance Programme (IDSP) data and to determine the feasibility of using Google Trends for the prediction of outbreaks or epidemics. Methods: The Google search queries related to malaria, dengue fever, chikungunya, and enteric fever for Chandigarh union territory and Haryana state of India in 2016 were extracted and compared with presumptive form data of the IDSP. Spearman correlation and scatter plots were used to depict the statistical relationship between the two datasets. Time trend plots were constructed to assess the correlation between Google search trends and disease notification under the IDSP. Results: Temporal correlation was observed between the IDSP reporting and Google search trends. Time series analysis of the Google Trends showed strong correlation with the IDSP data with a lag of -2 to -3 weeks for chikungunya and dengue fever in Chandigarh (r > 0.80) and Haryana (r > 0.70). Malaria and enteric fever showed a lag period of -2 to -3 weeks with moderate correlation. Conclusions: Similar results were obtained when applying the results of previous studies to specific diseases, and it is considered that many other diseases should be studied at the national and sub-national levels.

• 한국의류학회지
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• 제32권7호
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• pp.1046-1055
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• 2008

The purpose of this study is to investigate the effects of product variables(i.e., fashionability, brand fame) and situational variables(i.e., time constraint, usage occasion) on the internet browsing behavior (i.e., prepurchase search; ongoing search for products, stores, fashion trends, promotions, and post-purchasing information) for fashion products. This study developed hypothetical scenarios based on the factorial design of the two independent variables for the survey with a questionnaire. All items in the questionnaire were measured on a six-point scale. By convenience sampling and on-line survey, a total of 209 usable responses were used for further analyses. The results show as follows; firstly, respondents more intensively browse for information search on products, stores and fashion trends when the style of browsing products is trendy. Secondly, whether the fashion products have a famous brand name or not has a significant influence on browsing behavior for stores and fashion trends information search. Thirdly, time constraint is found to influence significantly on respondents' browsing for promotions information search. Lastly, occasion for product worn shows a significant influence on browsing behavior for stores and fashion trends information search. The managerial implications are provided based on findings.