Understanding internet health search patterns: An early exploration into the usefulness of Google Trends

Ellery, Peter J.; Vaughn, William R.; Ellery, Jane; Bott, Jennifer P.; Ritchey, Kristin; Byers, Lori

doi:10.1179/175380608790912905

Cited by 8 publications

(8 citation statements)

References 6 publications

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“…In comparison, the R-value in this research is relatively high (R-values ranging from 0.921 to 0.937). The high correlation between official dengue reports and Google Trends data in this study is different from Alicino, Cervellin, and Ellery's research finding [15,16,21]. Those researches found a disassociation between Google Trends data and disease occurrence and also found that Google Trends data is more influenced by media coverage than actual epidemiological burden.…”

Section: Discussioncontrasting

confidence: 99%

Correlation between Google Trends on dengue fever and national surveillance report in Indonesia

Husnayain

Fuad

Lazuardi

2019

Global Health Action

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Background: Digital traces are rapidly used for health monitoring purposes in recent years. This approach is growing as the consequence of increased use of mobile phone, Internet, and machine learning. Many studies reported the use of Google Trends data as a potential data source to assist traditional surveillance systems. The rise of Internet penetration (54.7%) and the huge utilization of Google (98%) indicate the potential use of Google Trends in Indonesia. No study was performed to measure the correlation between country wide official dengue reports and Google Trends data in Indonesia. Objective: This study aims to measure the correlation between Google Trends data on dengue fever and the Indonesian national surveillance report. Methods: This research was a quantitative study using time series data (2012–2016). Two sets of data were analyzed using Moving Average analysis in Microsoft Excel. Pearson and Time lag correlations were also used to measure the correlation between those data. Results: Moving Average analysis showed that Google Trends data have a linear time series pattern with official dengue report. Pearson correlation indicated high correlation for three defined search terms with R-value range from 0.921 to 0.937 (p ≤ 0.05, overall period) which showed increasing trend in epidemic periods (2015–2016). Time lag correlation also indicated that Google Trends data can potentially be used for an early warning system and novel tool to monitor public reaction before the increase of dengue cases and during the outbreak. Conclusions: Google Trends data have a linear time series pattern and statistically correlated with annual official dengue reports. Identification of information-seeking behavior is needed to support the use of Google Trends for disease surveillance in Indonesia.

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Section: Discussioncontrasting

confidence: 99%

Correlation between Google Trends on dengue fever and national surveillance report in Indonesia

Husnayain

Fuad

Lazuardi

2019

Global Health Action

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“…measured saliency related to the Zika virus by examining fluxuations in the volume of Zika messages on Twitter on a particular day (Southwell, Dolina, Jimenez‐Magdaleno, Squiers, & Kelly, ). Saliency has also been measured using Google Trends data (Ellery et al., ), which captures the number of searches per day on a given term, in a specified geographic area (Noar, Althouse, Ayers, Francis, & Ribisl, ). Although, to our knowledge, salience has not often been studied in the context of message passing, at least one study suggests that salience influences the passing of risk messages.…”

Section: Social Media and Risk Communicationmentioning

confidence: 99%

Retweeting Risk Communication: The Role of Threat and Efficacy

et al. 2018

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Social media platforms like Twitter and Facebook provide risk communicators with the opportunity to quickly reach their constituents at the time of an emerging infectious disease. On these platforms, messages gain exposure through message passing (called "sharing" on Facebook and "retweeting" on Twitter). This raises the question of how to optimize risk messages for diffusion across networks and, as a result, increase message exposure. In this study we add to this growing body of research by identifying message-level strategies to increase message passing during high-ambiguity events. In addition, we draw on the extended parallel process model to examine how threat and efficacy information influence the passing of Zika risk messages. In August 2016, we collected 1,409 Twitter messages about Zika sent by U.S. public health agencies' accounts. Using content analysis methods, we identified intrinsic message features and then analyzed the influence of those features, the account sending the message, the network surrounding the account, and the saliency of Zika as a topic, using negative binomial regression. The results suggest that severity and efficacy information increase how frequently messages get passed on to others. Drawing on the results of this study, previous research on message passing, and diffusion theories, we identify a framework for risk communication on social media. This framework includes four key variables that influence message passing and identifies a core set of message strategies, including message timing, to increase exposure to risk messages on social media during high-ambiguity events.

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“…Fluctuations in search volume may reveal the influence of common factors on the population. 17,18 In the case of health-related searches, it is possible that some event has brought about increased interest in a health condition across the population.…”

Section: Introductionmentioning

confidence: 99%

“…19–21 Alternatively, mentions of migraine in the media may increase interest in and awareness of migraine and lead to increased searches. 17 By identifying social predictors of migraines, we can better understand the complex patterns in migraine search volume. The awareness of societal trends can help forecast future migraine fluctuations, and the application of these forecasts could help prepare healthcare providers for the expected volume of incoming migraine patients.…”

Section: Introductionmentioning

confidence: 99%

Using Search Engines to Investigate Shared Migraine Experiences

et al. 2017

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Objectives To investigate migraine patterns in the United States using Google search data and utilize this information to better understand societal-level trends. Additionally, we aimed to evaluate time-series relationships between migraines and social factors. Background Extensive research has been done on clinical factors associated with migraines, yet population-level social factors have not been widely explored. Migraine internet search data may provide insight into migraine trends beyond information that can be gleaned from other sources. Methods In this longitudinal analysis of open access data, we performed a time-series analysis in which about 12 years of Google Trends data (January 1st 2004 to August 15th 2016) was assessed. Data points were captured at a daily level and Google’s 0 to 100 adjusted scale was used as the primary outcome to enable the comparison of relative popularity in the migraine search term. We hypothesized that the volume of relative migraine Google searches would be affected by societal aspects such as day of the week, holidays, and novel social events. Results Several recurrent social factors that drive migraine searches were identified. Of these, day of the week had the most significant impact on the volume of Google migraine searches. On average, Mondays accumulated 13.31 higher relative search volume than Fridays (95% CI: 11.12 to 15.51, p=<0.001). Surprisingly, holidays were associated with lower relative migraine search volumes. Christmas Day had 13.84 lower relative search volumes (95% CI: 6.26 to 21.43, p=<0.001) and Thanksgiving had 20.18 lower relative search volumes (95% CI: 12.55 to 27.82, p=<0.001) than days that were not holidays. Certain novel social events and extreme weather also appear to be associated with relative migraine Google search volume. Conclusions Social factors play a crucial role in explaining population level migraine patterns, and thus, warrant further exploration.

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Understanding internet health search patterns: An early exploration into the usefulness of Google Trends

Cited by 8 publications

References 6 publications

Correlation between Google Trends on dengue fever and national surveillance report in Indonesia

Correlation between Google Trends on dengue fever and national surveillance report in Indonesia

Retweeting Risk Communication: The Role of Threat and Efficacy

Using Search Engines to Investigate Shared Migraine Experiences

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