It Is Time to Prepare for the Future: Forecasting Social Trends

Han, Soyeon Caren; Chung, Hyunsuk; Kang, Byeong Ho

doi:10.1007/978-3-642-35603-2_48

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…Online search traffic data have been suggested to be a good analyzer of internet behavior, while Google Trends acts as a reliable tool in predicting changes in human behavior; subject to careful selection of the searched-for terms, Google data can accurately measure the public’s interest [ 12 ]. Google Trends provides the field of big data with new opportunities, as it has been shown to be valid [ 13 ] and has been proven valuable [ 14 , 15 ], accurate [ 16 ], and beneficial [ 17 ] for forecasting. Therefore, great potential arises from using Web-based queries to examine topics and issues that would have been difficult or even impossible to explore without the use of big data.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review

Mavragani¹,

Ochoa²,

Tsagarakis³

2018

J Med Internet Res

283

211

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BackgroundIn the era of information overload, are big data analytics the answer to access and better manage available knowledge? Over the last decade, the use of Web-based data in public health issues, that is, infodemiology, has been proven useful in assessing various aspects of human behavior. Google Trends is the most popular tool to gather such information, and it has been used in several topics up to this point, with health and medicine being the most focused subject. Web-based behavior is monitored and analyzed in order to examine actual human behavior so as to predict, better assess, and even prevent health-related issues that constantly arise in everyday life.ObjectiveThis systematic review aimed at reporting and further presenting and analyzing the methods, tools, and statistical approaches for Google Trends (infodemiology) studies in health-related topics from 2006 to 2016 to provide an overview of the usefulness of said tool and be a point of reference for future research on the subject.MethodsFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for selecting studies, we searched for the term “Google Trends” in the Scopus and PubMed databases from 2006 to 2016, applying specific criteria for types of publications and topics. A total of 109 published papers were extracted, excluding duplicates and those that did not fall inside the topics of health and medicine or the selected article types. We then further categorized the published papers according to their methodological approach, namely, visualization, seasonality, correlations, forecasting, and modeling.ResultsAll the examined papers comprised, by definition, time series analysis, and all but two included data visualization. A total of 23.1% (24/104) studies used Google Trends data for examining seasonality, while 39.4% (41/104) and 32.7% (34/104) of the studies used correlations and modeling, respectively. Only 8.7% (9/104) of the studies used Google Trends data for predictions and forecasting in health-related topics; therefore, it is evident that a gap exists in forecasting using Google Trends data.ConclusionsThe monitoring of online queries can provide insight into human behavior, as this field is significantly and continuously growing and will be proven more than valuable in the future for assessing behavioral changes and providing ground for research using data that could not have been accessed otherwise.

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Section: Introductionmentioning

confidence: 99%

Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review

Mavragani¹,

Ochoa²,

Tsagarakis³

2018

J Med Internet Res

283

211

View full text Add to dashboard Cite

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“…Google Trends data have been previously shown to be valid by many studies [ 44 ], and work on the subject has shown the tool’s contribution to forecasting [ 45 - 46 ] and analysis of online behavior, provided careful selection of the examined terms [ 47 ]. The aim of this paper is to examine if nowcasting asthma prevalence in the United States is possible using online search traffic data.…”

Section: Introductionmentioning

confidence: 99%

Integrating Smart Health in the US Health Care System: Infodemiology Study of Asthma Monitoring in the Google Era

Mavragani¹,

Sampri²,

Sypsa³

et al. 2018

JMIR Public Health Surveill

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BackgroundWith the internet’s penetration and use constantly expanding, this vast amount of information can be employed in order to better assess issues in the US health care system. Google Trends, a popular tool in big data analytics, has been widely used in the past to examine interest in various medical and health-related topics and has shown great potential in forecastings, predictions, and nowcastings. As empirical relationships between online queries and human behavior have been shown to exist, a new opportunity to explore the behavior toward asthma—a common respiratory disease—is present.ObjectiveThis study aimed at forecasting the online behavior toward asthma and examined the correlations between queries and reported cases in order to explore the possibility of nowcasting asthma prevalence in the United States using online search traffic data.MethodsApplying Holt-Winters exponential smoothing to Google Trends time series from 2004 to 2015 for the term “asthma,” forecasts for online queries at state and national levels are estimated from 2016 to 2020 and validated against available Google query data from January 2016 to June 2017. Correlations among yearly Google queries and between Google queries and reported asthma cases are examined.ResultsOur analysis shows that search queries exhibit seasonality within each year and the relationships between each 2 years’ queries are statistically significant (P<.05). Estimated forecasting models for a 5-year period (2016 through 2020) for Google queries are robust and validated against available data from January 2016 to June 2017. Significant correlations were found between (1) online queries and National Health Interview Survey lifetime asthma (r=–.82, P=.001) and current asthma (r=–.77, P=.004) rates from 2004 to 2015 and (2) between online queries and Behavioral Risk Factor Surveillance System lifetime (r=–.78, P=.003) and current asthma (r=–.79, P=.002) rates from 2004 to 2014. The correlations are negative, but lag analysis to identify the period of response cannot be employed until short-interval data on asthma prevalence are made available.ConclusionsOnline behavior toward asthma can be accurately predicted, and significant correlations between online queries and reported cases exist. This method of forecasting Google queries can be used by health care officials to nowcast asthma prevalence by city, state, or nationally, subject to future availability of daily, weekly, or monthly data on reported cases. This method could therefore be used for improved monitoring and assessment of the needs surrounding the current population of patients with asthma.

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