Cancer Internet Search Activity on a Major Search Engine, United States 2001-2003

Cooper, Crystale Purvis; Mallon, Kenneth P; Leadbetter, Steven; Pollack, Lori A.; Peipins, Lucy A.

doi:10.2196/jmir.7.3.e36

Cited by 112 publications

(101 citation statements)

References 23 publications

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“…The same year Cooper, Mallon, Leadbetter, Pollack & Peipins [10] described the use of Internet search volume for cancer-related topics. Since then there have been several papers that have examined web search data in numerous fields.…”

Section: Using Google Searches Totals To Predict Social Trendsmentioning

confidence: 99%

Google Searches and Suicide Rates in Spain, 2004-2013: Correlation Study (Preprint)

Jiménez¹,

Germán²,

Ramos³

2018

Preprint

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BACKGROUND Different studies have suggested that web search data are useful in forecasting several phenomena from the field of economics to epidemiology or health issues. OBJECTIVE This study aimed to (1) evaluate the correlation between suicide rates released by the Spanish National Statistics Institute (INE) and internet search trends in Spain reported by Google Trends (GT) for 57 suicide-related terms representing major known risks of suicide and an analysis of these results using a linear regression model and (2) study the differential association between male and female suicide rates published by the INE and internet searches of these 57 terms. METHODS The study period was from 2004 to 2013. In this study, suicide data were collected from (1) Spain’s INE and (2) local internet search data from GT, both from January 2004 to December 2013. We investigated and validated 57 suicide-related terms already tested in scientific studies before 2015 that would be the best predictors of new suicide cases. We then evaluated the nowcasting effects of a GT search through a cross-correlation analysis and by linear regression of the suicide incidence data with the GT data. RESULTS Suicide rates in Spain in the study period were positively associated (r<-0.2) for the general population with the search volume for 7 terms and negatively for 1 from the 57 terms used in previous studies. Suicide rates for men were found to be significantly different than those of women. The search term, “allergy,” demonstrated a lead effect for new suicide cases (r=0.513; P=.001). The next significant correlating terms for those 57 studied were “antidepressant,” “alcohol abstinence,” “relationship breakup” (r=0.295, P=.001; r=0.295, P=.001; and r=0.268, P=.002, respectively). Significantly different results were obtained for men and women. Search terms that correlate with suicide rates of women are consistent with previous studies, showing that the incidence of depression is higher in women than in men, and showing different gender searching patterns. CONCLUSIONS A better understanding of internet search behavior of both men and women in relation to suicide and related topics may help design effective suicide prevention programs based on information provided by search robots and other big data sources.

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Section: Using Google Searches Totals To Predict Social Trendsmentioning

confidence: 99%

Google Searches and Suicide Rates in Spain, 2004-2013: Correlation Study (Preprint)

Jiménez¹,

Germán²,

Ramos³

2018

Preprint

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“…When appropriately trained, these methods can be quite accurate; for example, many of the cited models can produce near real-time estimates of case counts with correlations upwards of r = 0.95. The collection of disease surveillance work cited above has estimated incidence for a wide variety of infectious and noninfectious conditions: avian influenza [52], cancer [55], chicken pox [67], cholera [81], dengue [50,53,84], dysentery [76], gastroenteritis [56,61,67], gonorrhea [64], hand foot and mouth disease (HFMD) [72], HIV/AIDS [75,76], influenza [34,36,54,57,59,62,63,65,67,68,71,74,[77][78][79][80]82,83,[85][86][87][88][89][90][91][92][93], kidney stones [51], listeriosis [70], malaria [66], methicillin-resistant Staphylococcus aureus (MRSA) [58]<...>…”

Section: Author Summarymentioning

confidence: 99%

“…Openness. Deep access to search queries from Baidu, a Chinese-language search engine serving mostly the Chinese market (http://www.baidu.com) [64,74,76]; Google [36,[50][51][52][53][54][56][57][58][59][60][65][66][67][69][70][71][72][73]75]; Yahoo [55,68]; and Yandex, a search engine serving mostly Russia and Slavic countries in Russian (http://www.yandex.ru), English (http://www. yandex.com), and Turkish (http://www.yandex.com.tr) [75], as well as purpose-built health website search queries [61][62][63] and access logs [72,93] are available only to those within the organizations, upon payment of an often-substantial fee, or by some other special arrangement.…”

Section: Author Summarymentioning

confidence: 99%

Global Disease Monitoring and Forecasting with Wikipedia

Generous

Fairchild²,

Deshpande³

et al. 2016

OJPHI

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Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with r 2 up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without retraining is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

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“…As explained in Choi and Varian (2012), the pioneer studies that advocate the usefulness of web search data in forecasting macroeconomic statistics (Ettredge, Gerdes, & Karuga, 2005) and presenting Internet search activity as an innovative tool for passive surveillance of health information-seeking behavior (Cooper, Mallon, Leadbetter, Pollack, & Peipins, 2005) were followed by studies in epidemiology (Polgreen, Chen, Pennock, Nelson, & Weinstein, 2008;Ginsberg et al, 2009) that showed that search data could predict the incidence of influenza-like diseases. Da, Engelberg, and Gao (2011) propose the Google Trends search volume index as direct measure for investor attention while Vosen and Schmidt (2011) establish that Google Trends is a very promising new source of data to forecast private consumption, since in almost all experiments conducted the Google Indicators' in-sample and out-of-sample predictive power proved to be better than that of the conventional survey-based indicators.…”

Section: Online and Traditional Sentiment Analysismentioning

confidence: 99%

Reforming Higher Education Finance in Turkey: The Alumni - Crowdfunded Student Debt Fund “A-CSDF” Model

Son-Turan

2016

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KeywordsThis study presents an innovative and sustainable system for mobilizing Turkish university alumni to contribute to a crowdfunded pool repackaged as a student debt instrument with an elaborate performance tracking tool, various payoff structures and income-contingent repayment schedules. The ultimate aim is to offer a remedy for the conspicuous global shortage of alternative finance sources and various forms of aid to higher education students in the short-term, and, through enabling equitable and egalitarian access to quality higher education, transforming society and enhancing economic development in the longer-term. The model rests upon a six-dimensional framework and its infrastructure is facilitated by a newly emerged form of digitally enhanced financing, "crowdfunding". The research method involves content analysis and data triangulation for validation purposes to determine the sub-themes surrounding the higher education problem in Turkey. The theme-driven keywords are searched for on Turkey's first original social network, Eksi Sozluk, to uncover trends and biases towards student loans, debt repayment and associated concepts. Subsequently, the same keywords are utilized in a Google Trends search volume analysis, and are finally validated by a focus group discussion. The theoretical framework to explain students' attitudes towards borrowing and loan repayment and the motivation behind alumni and charitable giving, rests mainly on behavioral economics. The A-CDSF Model uniquely addresses the higher education finance problem in Turkey and offers an easily implementable original solution for institutions and policy makers.Higher education financing Student debtIncome-contigent debt Crowdfunding Alumni givingArticle Info

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Cancer Internet Search Activity on a Major Search Engine, United States 2001-2003

Cited by 112 publications

References 23 publications

Google Searches and Suicide Rates in Spain, 2004-2013: Correlation Study (Preprint)

Google Searches and Suicide Rates in Spain, 2004-2013: Correlation Study (Preprint)

Global Disease Monitoring and Forecasting with Wikipedia

Reforming Higher Education Finance in Turkey: The Alumni - Crowdfunded Student Debt Fund “A-CSDF” Model

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