They’re heating up: Internet search query trends reveal significant public interest in heat-not-burn tobacco products

ObjectiveThis study explored the changes of global public interest in internet search of ankylosing spondylitis (AS) based on Google Trends (GT) data, in order to reflect the characteristics of AS itself.MethodsGT was used to obtain the search popularity scores of the term ’AS’ on a global scale, between January 2004 and December 2018, under the ’health’ classification. Based on the global search data of AS provided by GT, the cosinor analysis was used to test whether there was seasonality in AS.ResultsIn general, AS related search volume demonstrated a decreasing trend from January 2004 to December 2014 and then remain stable from January 2015 to December 2018. No obvious seasonal variations were detected in AS related search volume (amplitude=1.54; phase: month=3.9; low point: month=9.9; p>0.025), which peaked in April and bottomed out in October. The top 17 rising topics were adalimumab, spondylolisthesis, Morbus, Vladimir Mikhailovich Bekhterev, autoimmune disease, rheumatoid arthritis, ankylosis, HLA- B27 positive, Crohn’s disease, rheumatology, spondylosis, arthritis, uveitis, rheumatism, sacroiliac, psoriatic arthritis and spondylitis.ConclusionsGlobally, there is no significant seasonal variation in GT for AS. The top fast-growing topics related to AS may be beneficial for doctors to provide targeted health education of the disease to patients and their families.

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Avian Influenza A (H7N9) and related Internet search query data in China

Scientific Reports ◽

10.1038/s41598-019-46898-y ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 10

Author(s):

Ying Chen ◽

Yuzhou Zhang ◽

Zhiwei Xu ◽

Xuanzhuo Wang ◽

Jiahai Lu ◽

...

Keyword(s):

Avian Influenza ◽

Influenza A ◽

Internet Search ◽

Search Query

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Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data

Influenza and Other Respiratory Viruses ◽

10.1111/irv.12376 ◽

2016 ◽

Vol 10 (4) ◽

pp. 314-323 ◽

Cited By ~ 21

Author(s):

Robert Moss ◽

Alexander Zarebski ◽

Peter Dawson ◽

James M. McCaw

Keyword(s):

Surveillance Data ◽

Internet Search ◽

Search Query ◽

Influenza Outbreak

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Entity Type Disambiguation in User Queries

Journal of Information & Knowledge Management ◽

10.1142/s0219649211002948 ◽

2011 ◽

Vol 10 (03) ◽

pp. 209-224 ◽

Cited By ~ 1

Author(s):

Barbara Bazzanella ◽

Heiko Stoermer ◽

Paolo Bouquet

Keyword(s):

Information Search ◽

Bayesian Model ◽

Search Task ◽

Specific Information ◽

Internet Search ◽

Search Query ◽

Entity Search ◽

Beneficial Impact ◽

Primary Means ◽

Important Activity

Searching for information about individual entities such as persons, locations, events, is an important activity in Internet search today, and is in its core a very semantic-oriented task. Several ways for accessing such information exist, but for locating entity-specific information, search engines are the most commonly used approach. In this context, keyword queries are the primary means of retrieving information about a specific entity. We believe that an important first step of performing such a task is to understand what type of entity the user is looking for. We call this process Entity Type Disambiguation. In this paper, we present a Naive Bayesian Model for entity type disambiguation that explores our assumption that an entity type can be inferred from the attributes a user specifies in a search query. The model has been applied to queries provided by a large sample of participants in an experiment performing an entity search task. The beneficial impact of this approach for the development of new search systems is discussed.

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Monitoring public interest in solariums: variations in internet search volumes over time in Victoria

Australian and New Zealand Journal of Public Health ◽

10.1111/1753-6405.12053 ◽

2013 ◽

Vol 37 (3) ◽

pp. 292-292 ◽

Cited By ~ 1

Author(s):

Kimberley Dunstone ◽

Jennifer Makin ◽

Cairin Conway

Keyword(s):

Public Interest ◽

Internet Search ◽

Over Time

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Internet search query analysis can be used to demonstrate the rapidly increasing public awareness of palliative care in the USA

BMJ Supportive & Palliative Care ◽

10.1136/bmjspcare-2016-001171 ◽

2017 ◽

Vol 9 (1) ◽

pp. 40-44 ◽

Cited By ~ 4

Author(s):

Sarah McLean ◽

Paul Lennon ◽

Paul Glare

Keyword(s):

Palliative Care ◽

Public Awareness ◽

Search Term ◽

Internet Search ◽

Search Query ◽

Health Seeking ◽

The Public ◽

Health Related ◽

The Usa ◽

The Uk

BackgroundA lack of public awareness of palliative care (PC) has been identified as one of the main barriers to appropriate PC access. Internet search query analysis is a novel methodology, which has been effectively used in surveillance of infectious diseases, and can be used to monitor public awareness of health-related topics.ObjectivesWe aimed to demonstrate the utility of internet search query analysis to evaluate changes in public awareness of PC in the USA between 2005 and 2015.MethodsGoogle Trends provides a referenced score for the popularity of a search term, for defined regions over defined time periods. The popularity of the search term ‘palliative care’ was measured monthly between 1/1/2005 and 31/12/2015 in the USA and in the UK.ResultsResults were analysed using independent t-tests and joinpoint analysis. The mean monthly popularity of the search term increased between 2008–2009 (p<0.001), 2011–2012 (p<0.001), 2013–2014 (p=0.004) and 2014–2015 (p=0.002) in the USA. Joinpoint analysis was used to evaluate the monthly percentage change (MPC) in the popularity of the search term. In the USA, the MPC increase was 0.6%/month (p<0.05); in the UK the MPC of 0.05% was non-significant.DiscussionAlthough internet search query surveillance is a novel methodology, it is freely accessible and has significant potential to monitor health-seeking behaviour among the public. PC is rapidly growing in the USA, and the rapidly increasing public awareness of PC as demonstrated in this study, in comparison with the UK, where PC is relatively well established is encouraging in increasingly ensuring appropriate PC access for all.

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Monitoring COVID-19 related public Interest and population Health Literacy in South Asia: An Internet Search-Interest Based Model

10.1101/2020.08.24.20180943 ◽

2020 ◽

Cited By ~ 1

Author(s):

Hasan Symum ◽

Kh M. Ali Sagor

Keyword(s):

Health Literacy ◽

South Asia ◽

Population Health ◽

South Asian ◽

Public Interest ◽

Time Lag ◽

Personal Hygiene ◽

Internet Search ◽

Asian Countries ◽

Search Volume

AbstractBackgroundInformation epidemiology based on internet search data can be used to model COVID-19 pandemic progressions and monitor population health literacy. However, the applicability of internet searches to monitor COVID-19 infections and public health awareness in South Asian countries are unclear.ObjectivesTo assess the association of public interest and health literacy in COVID-19 with the number of infected cases South Asian countries.Material and MethodsGoogle Trends data from January to March 2020 were used to correlate public interest and literacy with official data on COVID-19 cases using the relative search volume (RSV) index. Public interest in COVID-19 was retrieved with the search topic “Coronavirus (Virus)”. Similarly, search terms “hand wash”, “face mask”, “hand sanitizer”, “face shield” and “gloves” were used to retrieve RSV indices as a surrogate of health literacy. Country-level correlation analyses were performed for a time lag between 30 and +30 days.ResultsThere were significant positive correlations between COVID-19 related public interest and daily confirmed cases in countries expect Nepal, Bhutan, and Sri Lanka. The highest public interest in South Asian Countries was on average 12 days before the local maximum of new confirmed cases. Similarly, web searches related to personal hygiene and preventive measures in south Asia correlated to the number of confirmed cases as well as national restriction measures.ConclusionPublic interest indicated by RSV indices can help to monitor the progression of an outbreak such as the current COVID-19 pandemic particularly in countries with a lack of diagnostic and surveillance capacity.

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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 ◽

10.7717/peerj.5134 ◽

2018 ◽

Vol 6 ◽

pp. e5134 ◽

Cited By ~ 10

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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Public interest in Cannabis during election season: a Google Trends analysis

Journal of Cannabis Research ◽

10.1186/s42238-020-00039-9 ◽

2020 ◽

Vol 2 (1) ◽

Author(s):

Trevor Torgerson ◽

Will Roberts ◽

Drew Lester ◽

Jam Khojasteh ◽

Matt Vassar

Keyword(s):

Climate Change ◽

Public Interest ◽

Presidential Election ◽

Presidential Elections ◽

Google Trends ◽

The Internet ◽

Evidence Based ◽

Internet Search ◽

The Past ◽

The Us

Abstract Introduction Given that 72% of internet users seek out health information using an internet search engine (Google being the most popular); we sought to investigate the public internet search interest in cannabis as a health topic when cannabis legislation appeared on state ballots and during presidential elections. Materials and methods We searched Google Trends for “cannabis” as a health topic. Google Trends data were extracted during the time period of May 1, 2008 to May 1, 2019 for the United States (US) and select states (18) within the US including: Alaska, Arizona, Arkansas, California, Colorado, Florida, Maine, Massachusetts, Michigan, Missouri, Nevada, North Dakota, Ohio, Oregon, Oklahoma, South Dakota, Utah, and Washington when cannabis was on the ballot. These state elections were referenda, not legislative votes. We then compared the internet search interest for cannabis before and after each election. To evaluate whether any associations with changes in the volume of cannabis internet searches were specific to the cannabis topic, or also occurred with other topics of general interest during an election year, the authors ran additional analyses of previously popular debated policies during Presidential Elections that may act as control topics. These policies included Education, Gun Control, Climate Change, Global Warming, and Abortion. We used the autoregressive integrated moving average (ARIMA) algorithm to forecast expected relative internet search interests for the 2012 and 2016 Presidential Elections. Individual variables were compared using a linear regression analysis for the beta coefficients performed in Stata Version 15.1 (StataCorp). Results Public internet search interest for “cannabis” increased during the voting month above the previous mean internet search interest for all 18 bills. For the US, observed internet search interest during each Presidential Election was 26.9% [95% CI, 18.4–35.4%] greater than expected in 2012 and 29.8% [95% CI, 20.8–38.8%] greater than expected in 2016. In 2016, significant state-level findings included an increase in relative internet search rates for cannabis in states with higher usage rates of cannabis in the past month (Coeff (95% CI), 3.4 (2.8–4.0)) and past month illicit drug use except cannabis rates (Coeff (95% CI), 17.4 (9.8–25.0)). Relative internet search rates for cannabis from 2008 to 2019 were also associated with increased cannabis usage in the past month (Coeff (95% CI), 3.1 (2.5–3.7)). States with higher access to legal cannabis were associated with higher relative internet search volumes for cannabis (Coeff (95% CI), 0.31 (0.15–0.46)). Of the five additional policies that were searched as topics, only two showed an increase in internet search interest during each Presidential Election. Climate Change increased by 3.5% [95% CI, − 13-20%] in 2012 and 20.1% [95% CI, 0–40%] in 2016 while Global Warming increased by 1.1% [95% CI, − 19-21%] in 2012 and 4.6% [95% CI, − 6-15%] in 2016. Conclusion Based on these results, we expect public interest in cannabis will spike prior to the Presidential election in 2020. Of the five selected control policies, only two showed an increase in internet search interest during both Presidential Elections and neither exceeded the internet search increase of cannabis. These results may indicate the growing awareness of cannabis in the US and mark a possible target for the timely dissemination of evidence-based information regarding cannabis and its usage/side-effects during future elections. Consequently, the results of this study may be important to physicians since they will likely receive an increased volume of questions relating to cannabis and its therapeutic uses during election season from interested patients. We recommend establishing a cannabis repository of evidence-based information, providing physician education, and a dosing guide be created to enable physicians to provide high quality care around the issue of cannabis.

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