Understanding Internet Health Search Patterns: How Useful is Google Trends?

2008 ◽  
Author(s):  
Peter J. Ellery ◽  
William H. Vaughn
2008 ◽  
Vol 1 (4) ◽  
pp. 441-456 ◽  
Author(s):  
Peter J. Ellery ◽  
William Vaughn ◽  
Jane Ellery ◽  
Jennifer Bott ◽  
Kristin Ritchey ◽  
...  

2021 ◽  
Vol 47 (5) ◽  
pp. 989-996
Author(s):  
Giovanni S. Marchini ◽  
Kauy V. M. Faria ◽  
Felippe L. Neto ◽  
Fábio César Miranda Torricelli ◽  
Alexandre Danilovic ◽  
...  

2020 ◽  
Author(s):  
Alberto Jimenez Jimenez ◽  
Rosa M Estevez-Reboredo ◽  
Miguel A Santed ◽  
Victoria Ramos

BACKGROUND COVID-19 is one of the biggest pandemics in human history, along with other disease pandemics, such as the H1N1 influenza A, bubonic plague, and smallpox pandemics. This study is a small contribution that tries to find contrasted formulas to alleviate global suffering and guarantee a more manageable future. OBJECTIVE In this study, a statistical approach was proposed to study the correlation between the incidence of COVID-19 in Spain and search data provided by Google Trends. METHODS We assessed the linear correlation between Google Trends search data and the data provided by the National Center of Epidemiology in Spain—which is dependent on the Instituto de Salud Carlos III—regarding the number of COVID-19 cases reported with a certain time lag. These data enabled the identification of anticipatory patterns. RESULTS In response to the ongoing outbreak, our results demonstrate that by using our correlation test, the evolution of the COVID-19 pandemic can be predicted in Spain up to 11 days in advance. CONCLUSIONS During the epidemic, Google Trends offers the possibility to preempt health care decisions in real time by tracking people's concerns through their search patterns. This can be of great help given the critical, if not dramatic need for complementary monitoring approaches that work on a population level and inform public health decisions in real time. This study of Google search patterns, which was motivated by the fears of individuals in the face of a pandemic, can be useful in anticipating the development of the pandemic.


2020 ◽  
Author(s):  
Alireza Ahmadvand ◽  
Ayda S. Forough ◽  
Lisa Nissen

BACKGROUND The public health crisis, due to the new Coronavirus found in December 2019, has received unprecedented attention from the public and the media. The infodemiological analysis of queries from search engines to assess the status of search interests and the actual burden of the new virus could be an informative approach. OBJECTIVE The aim of the study was to assess search query data from Google Trends, to visualize the interest in search over time for the new “Coronavirus” in Google, across four English-speaking countries, namely, Australia, Canada, the UK, and the USA, and compare the search interest with the actual burden of Coronavirus in the corresponding countries. METHODS We used Google Trends service to assess people’s interest in searching about “Coronavirus” classified as “Virus,” from January 1, 2020 to March 13, 2020 in Australia, Canada, the UK, and the USA. Then, we evaluated top regions and their relative search volumes (SVs) and country-specific “Top” and “Rising” searches. We also evaluated the trends in the incidence of detected Coronavirus infections to find possible differences between the actual burden of the disease and search patterns by the public. RESULTS From January 1, 2020 to March 13, 2020, Australia was the top country searching for Coronavirus in Google, followed by Canada, the UK, and the USA. There was a noticeable bimodal pattern in searching for Coronavirus, mostly in late January 2020, and then from early March 2020. Search interest in all four countries declined in the month of February 2020. Top regions in each of the four countries with the highest search interest where the ones which reported either a confirmed case of Coronavirus infection or a death due to it. None of the declarations by the World Health Organization of the nature of this pandemic appeared to have caused major changes in the search patterns in Google. CONCLUSIONS Search for ‘Coronavirus’ increased exponentially, in all four countries, mostly in Australia. The month of February 2020 could be considered a ‘lost opportunity’ in terms of acting on the momentum of searching by people on Google about the Coronavirus. The increased interest in searching for keywords related to Coronavirus and its symptoms shows the possible focus areas of awareness campaigns in increasing societal demand for health information on the Web, to be met in community-wide communication or awareness interventions, should another pandemic occur in the future. 


Author(s):  
Joseph Alexander Paguio ◽  
Jasper Seth Yao ◽  
Ma. Sophia Graciela L. Reyes ◽  
Grace Lee ◽  
Edward Christopher Dee

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Amaryllis Mavragani ◽  
Konstantinos Gkillas ◽  
Konstantinos P. Tsagarakis

Abstract 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.


2018 ◽  
Vol 5 (7) ◽  
pp. 172080
Author(s):  
Nicolas Scrutton Alvarado ◽  
Tyler J. Stevenson

There has been an exponential growth of information seeking behaviour (ISB) via Internet-based programs over the past decade. The availability of software that record ISB temporal patterns has provided a valuable opportunity to examine biological rhythms in human behaviour. Internet search repositories, such as Google Trends, permit the analyses of large datasets that can be used to track ISB on a domestic and international scale. We examined daily and seasonal Google Trends search patterns for keywords related to food intake, using the most relevant search terms for the USA, UK, Canada, India and Australia. Daily and seasonal ISB rhythmicity were analysed using CircWave v. 1.4. Daily ISB data revealed a robust and significant sine waveform for general terms (e.g. ‘pizza delivery') and country-specific search terms (e.g. ‘just eat'). The pattern revealed clear evening double-peaks, occurring every day at 19.00 and 02.00. The patterns were consistent across search terms, days of the week and geographical locations, suggesting a common ISB rhythm that is not necessarily culture-dependent. Then, we conducted Cosinor v. 2.4 analyses to examine the daily amplitudes in ISB. The results indicated a non-significant linear increased from Monday to Sunday. Seasonal data did not show consistent significant ISB patterns. It is likely that two different human populations are responsible for the daily ‘early' and ‘late' evening ISB peaks. We propose that the major factor that contributes to the bimodal evening peak is age-dependent (e.g. adolescent, early adulthood versus midlife and mature adulthood) and a minor role for human chronotypes (e.g. late versus early). Overall, we present novel human appetitive behaviour for information seeking of food resources and propose that Internet-based search patterns reflect a biological rhythm of motivation for energy balance.


10.2196/23518 ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. e23518 ◽  
Author(s):  
Alberto Jimenez Jimenez ◽  
Rosa M Estevez-Reboredo ◽  
Miguel A Santed ◽  
Victoria Ramos

Background COVID-19 is one of the biggest pandemics in human history, along with other disease pandemics, such as the H1N1 influenza A, bubonic plague, and smallpox pandemics. This study is a small contribution that tries to find contrasted formulas to alleviate global suffering and guarantee a more manageable future. Objective In this study, a statistical approach was proposed to study the correlation between the incidence of COVID-19 in Spain and search data provided by Google Trends. Methods We assessed the linear correlation between Google Trends search data and the data provided by the National Center of Epidemiology in Spain—which is dependent on the Instituto de Salud Carlos III—regarding the number of COVID-19 cases reported with a certain time lag. These data enabled the identification of anticipatory patterns. Results In response to the ongoing outbreak, our results demonstrate that by using our correlation test, the evolution of the COVID-19 pandemic can be predicted in Spain up to 11 days in advance. Conclusions During the epidemic, Google Trends offers the possibility to preempt health care decisions in real time by tracking people's concerns through their search patterns. This can be of great help given the critical, if not dramatic need for complementary monitoring approaches that work on a population level and inform public health decisions in real time. This study of Google search patterns, which was motivated by the fears of individuals in the face of a pandemic, can be useful in anticipating the development of the pandemic.


2016 ◽  
Vol 10 (2) ◽  
pp. 152-159 ◽  
Author(s):  
Nigel Phelan ◽  
Shane Davy ◽  
Gerard W. O'Keeffe ◽  
Denis S. Barry

2020 ◽  
Vol 51 (2) ◽  
pp. 479-493
Author(s):  
Jenny A. Roberts ◽  
Evelyn P. Altenberg ◽  
Madison Hunter

Purpose The results of automatic machine scoring of the Index of Productive Syntax from the Computerized Language ANalysis (CLAN) tools of the Child Language Data Exchange System of TalkBank (MacWhinney, 2000) were compared to manual scoring to determine the accuracy of the machine-scored method. Method Twenty transcripts of 10 children from archival data of the Weismer Corpus from the Child Language Data Exchange System at 30 and 42 months were examined. Measures of absolute point difference and point-to-point accuracy were compared, as well as points erroneously given and missed. Two new measures for evaluating automatic scoring of the Index of Productive Syntax were introduced: Machine Item Accuracy (MIA) and Cascade Failure Rate— these measures further analyze points erroneously given and missed. Differences in total scores, subscale scores, and individual structures were also reported. Results Mean absolute point difference between machine and hand scoring was 3.65, point-to-point agreement was 72.6%, and MIA was 74.9%. There were large differences in subscales, with Noun Phrase and Verb Phrase subscales generally providing greater accuracy and agreement than Question/Negation and Sentence Structures subscales. There were significantly more erroneous than missed items in machine scoring, attributed to problems of mistagging of elements, imprecise search patterns, and other errors. Cascade failure resulted in an average of 4.65 points lost per transcript. Conclusions The CLAN program showed relatively inaccurate outcomes in comparison to manual scoring on both traditional and new measures of accuracy. Recommendations for improvement of the program include accounting for second exemplar violations and applying cascaded credit, among other suggestions. It was proposed that research on machine-scored syntax routinely report accuracy measures detailing erroneous and missed scores, including MIA, so that researchers and clinicians are aware of the limitations of a machine-scoring program. Supplemental Material https://doi.org/10.23641/asha.11984364


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