Online Search Traffic Data Research Articles

Predictability analysis of the Pound\u2019s Brexit exchange rates based on Google Trends data

During the last decade, the use of online search traffic data is becoming popular in examining, analyzing, and predicting human behavior, with Google Trends being a popular tool in monitoring and analyzing the users' online search patterns in several research areas, like health, medicine, politics, economics, and finance. Towards the direction of exploring the Sterling Pound’s predictability, we employ Google Trends data from the last 5 years (March 1st, 2015 to February 29th, 2020) and perform predictability analysis on the Pound’s exchange rates to Euro and Dollar. The period selected includes the 2016 UK referendum as well as the actual Brexit day (January 31st, 2020), with the analysis aiming at analyzing the Pound’s relationships with Google query data on Pound-related keywords and topics. A quantile dependence method is employed, i.e., cross-quantilograms, to test for directional predictability from Google Trends data to the Pound’s exchange rates for lags from zero to 30 (in weeks). The results indicate that statistically significant quantile dependencies exist between Google query data and the Pound’s exchange rates, which point to the direction of one of the main implications in this field, that is to examine whether the movements in one economic variable can cause reactions in other economic variables.

Tracking COVID-19 in Europe: Infodemiology Approach.

BackgroundInfodemiology (ie, information epidemiology) uses web-based data to inform public health and policy. Infodemiology metrics have been widely and successfully used to assess and forecast epidemics and outbreaks.ObjectiveIn light of the recent coronavirus disease (COVID-19) pandemic that started in Wuhan, China in 2019, online search traffic data from Google are used to track the spread of the new coronavirus disease in Europe.MethodsTime series from Google Trends from January to March 2020 on the Topic (Virus) of “Coronavirus” were retrieved and correlated with official data on COVID-19 cases and deaths worldwide and in the European countries that have been affected the most: Italy (at national and regional level), Spain, France, Germany, and the United Kingdom.ResultsStatistically significant correlations are observed between online interest and COVID-19 cases and deaths. Furthermore, a critical point, after which the Pearson correlation coefficient starts declining (even if it is still statistically significant) was identified, indicating that this method is most efficient in regions or countries that have not yet peaked in COVID-19 cases.ConclusionsIn the past, infodemiology metrics in general and data from Google Trends in particular have been shown to be useful in tracking and forecasting outbreaks, epidemics, and pandemics as, for example, in the cases of the Middle East respiratory syndrome, Ebola, measles, and Zika. With the COVID-19 pandemic still in the beginning stages, it is essential to explore and combine new methods of disease surveillance to assist with the preparedness of health care systems at the regional level.

Google Trends in Infodemiology and Infoveillance: Methodology Framework.

Internet data are being increasingly integrated into health informatics research and are becoming a useful tool for exploring human behavior. The most popular tool for examining online behavior is Google Trends, an open tool that provides information on trends and the variations of online interest in selected keywords and topics over time. Online search traffic data from Google have been shown to be useful in analyzing human behavior toward health topics and in predicting disease occurrence and outbreaks. Despite the large number of Google Trends studies during the last decade, the literature on the subject lacks a specific methodology framework. This article aims at providing an overview of the tool and data and at presenting the first methodology framework in using Google Trends in infodemiology and infoveillance, including the main factors that need to be taken into account for a strong methodology base. We provide a step-by-step guide for the methodology that needs to be followed when using Google Trends and the essential aspects required for valid results in this line of research. At first, an overview of the tool and the data are presented, followed by an analysis of the key methodological points for ensuring the validity of the results, which include selecting the appropriate keyword(s), region(s), period, and category. Overall, this article presents and analyzes the key points that need to be considered to achieve a strong methodological basis for using Google Trends data, which is crucial for ensuring the value and validity of the results, as the analysis of online queries is extensively integrated in health research in the big data era.

Predicting referendum results in the Big Data Era

In addressing the challenge of Big Data Analytics, what has been of notable significance is the analysis of online search traffic data in order to analyze and predict human behavior. Over the last decade, since the establishment of the most popular such tool, Google Trends, the use of online data has been proven valuable in various research fields, including -but not limited to- medicine, economics, politics, the environment, and behavior. In the field of politics, given the inability of poll agencies to always well approximate voting intentions and results over the past years, what is imperative is to find new methods of predicting elections and referendum outcomes. This paper aims at presenting a methodology of predicting referendum results using Google Trends; a method applied and verified in six separate occasions: the 2014 Scottish Referendum, the 2015 Greek Referendum, the 2016 UK Referendum, the 2016 Hungarian Referendum, the 2016 Italian Referendum, and the 2017 Turkish Referendum. Said referendums were of importance for the respective country and the EU as well, and received wide international attention. Google Trends has been empirically verified to be a tool that can accurately measure behavioral changes as it takes into account the users’ revealed and not the stated preferences. Thus we argue that, in the time of intelligence excess, Google Trends can well address the analysis of social changes that the internet brings.

Forecasting AIDS prevalence in the United States using online search traffic data

Over the past decade and with the increasing use of the Internet, the assessment of health issues using online search traffic data has become an integral part of Health Informatics. Internet data in general and from Google Trends in particular have been shown to be valid and valuable in predictions, forecastings, and nowcastings; and in detecting, tracking, and monitoring diseases’ outbreaks and epidemics. Empirical relationships have been shown to exist between Google Trends’ data and official data in several health topics, with the science of infodemiology using the vast amount of information available online for the assessment of public health and policy matters. The aim of this study is to provide a method of forecasting AIDS prevalence in the US using online search traffic data from Google Trends on AIDS related terms. The results at first show that significant correlations between Google Trends’ data and official health data on AIDS prevalence (2004–2015) exist in several States, while the estimated forecasting models for AIDS prevalence show that official health data and Google Trends data on AIDS follow a logarithmic relationship. Overall, the results of this study support previous work on the subject suggesting that Google data are valid and valuable for the analysis and forecasting of human behavior towards health topics, and could further assist with Health Assessment in the US and in other countries and regions with valid available official health data.

Quantifying the UK Online Interest in Substances of the EU Watchlist for Water Monitoring: Diclofenac, Estradiol, and the Macrolide Antibiotics

Due to the increased interest in micropollutants, this paper aims at quantifying and analyzing the UK online interest in Diclofenac, Estradiol, Azithromycin, Clarithromycin, and Erythromycin, substances included in the EU watchlist for monitoring, in order to examine if the public’s online behavior and the use of these substances, in terms of issued prescriptions, are correlated. Using time series data from Google Trends from January 2004 to December 2014, an analysis of these substances in the UK, and in each UK region, i.e., England, Wales, Scotland, and Northern Ireland, is at first performed, followed by an analysis of interest by substance. The results show high interest in Diclofenac with a slight decline, while the Macrolides are significantly less popular though increasing. For Estradiol, the interest is low and declining throughout the examined period, in contrast to the scientific community, where Estradiol is the most studied substance. Prescription items and Google hits are highly correlated in the UK for Diclofenac, Azithromycin, and Clarithromycin, while no correlation is observed for Estradiol. Results from this study indicated that online search traffic data can be valuable in examining the public’s online behavior towards the monitored micropollutants, and could assist with the evaluation and forecasting of their concentrations in the waste, surface, and ground water in the UK.

Evaluating Google Trends as a Tool for Integrating the ‘Smart Health’ Concept in the Smart Cities’ Governance in USA

The aim of this paper is to introduce the methodology of using online search traffic data in order to integrate the public's online behavior in Smart Health; a concept that is currently rising concerning the health factor of Smart Cities. We use normalized data from Google Trends from January 2013 to December 2015 in the US, aiming at exploring the change in interest in various medical terms, and examine if Google Trends is a possible tool for evaluating health search queries by nowcasting the public's online interest. The results show that Google Trends’ data can be used for measuring the public's interest in health related terms, in order to assist with the evaluation of ‘Smart Health’.

Quantifying the Online Behavior Towards Organic Micropollutants of the EU Watchlist: The Cases of Diclofenac & the Macrolide Antibiotics

This study aims at quantifying and analyzing the online interest in the micropollutants Diclofenac and the Macrolide Antibiotics (Azithromycin, Clarithromycin and Erythromycin) included in the watchlist of the EU Decision 2015/495. Using online search traffic data from Google Trends, we examine the change in interest from 2004 to 2015 in five EU countries. The results show an increased Worldwide percentage change in interest in Diclofenac, Azithromycin and Clarithromycin over the selected period, in contrary to Erythromycin, that is declining. In the examined EU countries, Germany and the UK show the highest online interest with mostly increasing rates, while in France, Italy and Spain, the interest in all four substances is significantly lower.