scholarly journals Forecasting influenza in Hong Kong with Google search queries and statistical model fusion

PLoS ONE ◽  
2017 ◽  
Vol 12 (5) ◽  
pp. e0176690 ◽  
Author(s):  
Qinneng Xu ◽  
Yulia R. Gel ◽  
L. Leticia Ramirez Ramirez ◽  
Kusha Nezafati ◽  
Qingpeng Zhang ◽  
...  
Keyword(s):  
Hong Kong ◽  
Statistical Model ◽  
Model Fusion ◽  
Search Queries ◽  
Google Search

scholarly journals Google, Tell Me. Is He Gay? Masculinity, Heterosexuality, and Gendered Anxieties in Google Search Queries about Sexuality

2021 ◽  
pp. 073112142110019
Author(s):  
Emma Mishel ◽  
Tristan Bridges ◽  
Mónica L. Caudillo
Keyword(s):  
Social Desirability ◽  
Google Trends ◽  
Social Desirability Bias ◽  
Same Sex ◽  
Search Queries ◽  
Sexual Identities ◽  
Google Search

It is difficult to gauge people’s acceptance about same-sex sexualities, as responses to questionnaires are prone to social desirability bias. We offer a new proxy for understanding popular concern surrounding same-sex sexualities: prevalence of Google searches demonstrating concern over gay/lesbian sexual identities. Using Google Trends data, we find that Google searches about whether a specific person is gay or lesbian show patterned bias toward masculine searches, in that such searches are much more frequently conducted about boys and men compared with girls and women. We put these findings into context by comparing search frequencies with other popular Google searches about sexuality and otherwise. We put forth that the patterned bias toward masculine searches illustrates support for the enduring relationship between masculinity and heterosexuality and that it does so on a larger scale than previous research has been able to establish.

Google Search Queries About Neurosurgical Topics: Are They a Suitable Guide for Neurosurgeons?

2016 ◽  
Vol 90 ◽  
pp. 179-185 ◽  
Author(s):  
Anna C. Lawson McLean ◽  
Aaron Lawson McLean ◽  
Rolf Kalff ◽  
Jan Walter
Keyword(s):  
Search Queries ◽  
Google Search

scholarly journals The impact of political instability driven by the Tunisian revolution on the relationship between Google search queries index and financial market dynamics

2020 ◽  
Vol 4 (1) ◽  
pp. 61-76
Author(s):  
Yousra Trichilli ◽  
Mouna Boujelbène Abbes ◽  
Sabrine Zouari
Keyword(s):  
Stock Market ◽  
Political Instability ◽  
Investor Sentiment ◽  
Wavelet Coherence ◽  
Content Type ◽  
Search Queries ◽  
Market Return ◽  
Stock Market Return ◽  
Tunisian Revolution ◽  
Google Search

PurposeThis paper examines the impact of political instability on the investors' behavior, measured by Google search queries, and on the dynamics of stock market returns.Design/methodology/approachFirst, by using the DCC-GARCH model, the authors examine the effect of investor sentiment on the Tunisian stock market return. Second, the authors employ the fully modified dynamic ordinary least square method (FMOL) to estimate the long-term relationship between investor sentiment and Tunisian stock market return. Finally, the authors use the wavelet coherence model to test the co-movement between investor sentiment measured by Google Trends and Tunisian stock market return.FindingsUsing the dynamic conditional correlation (DCC), the authors find that Google search queries index has the ability to reflect political events especially the Tunisian revolution. In addition, empirical results of fully modified ordinary least square (FMOLS) method reveal that Google search queries index has a slightly higher effect on Tunindex return after the Tunisian revolution than before this revolution. Furthermore, by employing wavelet coherence model, the authors find strong comovement between Google search queries index and return index during the period of the Tunisian revolution political instability. Moreover, in the frequency domain, strong coherence can be found in less than four months and in 16–32 months during the Tunisian revolution which show that the Google search queries measure was leading over Tunindex return. In fact, wavelet coherence analysis confirms the result of DCC that Google search queries index has the ability to detect the behavior of Tunisian investors especially during the period of political instability.Research limitations/implicationsThis study provides empirical evidence to portfolio managers that may use Google search queries index as a robust measure of investor's sentiment to select a suitable investment and to make an optimal investments decisions.Originality/valueThe important research question of how political instability affects stock market dynamics has been neglected by scholars. This paper attempts principally to fill this void by investigating the time-varying interactions between market returns, volatility and Google search based index, especially during Tunisian revolution.

scholarly journals Use of social media, search queries, and demographic data to assess obesity prevalence in the United States

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Nina Cesare ◽  
Pallavi Dwivedi ◽  
Quynh C. Nguyen ◽  
Elaine O. Nsoesie
Keyword(s):  
Physical Activity ◽  
Social Media ◽  
Built Environment ◽  
Demographic Data ◽  
The United States ◽  
Obesity Prevalence ◽  
Male And Female ◽  
Search Queries ◽  
Environment Variables ◽  
Google Search

Abstract Obesity is a global epidemic affecting millions. Implementation of interventions to curb obesity rates requires timely surveillance. In this study, we estimated sex-specific obesity prevalence using social media, search queries, demographics and built environment variables. We collected 3,817,125 and 1,382,284 geolocated tweets on food and exercise respectively, from Twitter’s streaming API from April 2015 to March 2016. We also obtained searches related to physical activity and diet from Google Search Trends for the same time period. Next, we inferred the gender of Twitter users using machine learning methods and applied mixed-effects state-level linear regression models to estimate obesity prevalence. We observed differences in discussions of physical activity and foods, with males reporting higher intensity physical activities and lower caloric foods across 40 and 48 states, respectively. In addition, counties with the highest percentage of exercise and food tweets had lower male and female obesity prevalence. Lastly, our models separately captured overall male and female spatial trends in obesity prevalence. The average correlation between actual and estimated obesity prevalence was 0.797(95% CI, 0.796, 0.798) and 0.830 (95% CI, 0.830, 0.831) for males and females, respectively. Social media can provide timely community-level data on health information seeking and changes in behaviors, sentiments and norms. Social media data can also be combined with other data types such as, demographics, built environment variables, diet and physical activity indicators from other digital sources (e.g., mobile applications and wearables) to monitor health behaviors at different geographic scales, and to supplement delayed estimates from traditional surveillance systems.

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