Quantifying domestic violence in times of crisis: An internet search activity‐based measure for the COVID‐19 pandemic

2021 ◽  
Author(s):  
Dan Anderberg ◽  
Helmut Rainer ◽  
Fabian Siuda
Keyword(s):  
Domestic Violence ◽  
Internet Search ◽  
Search Activity

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

2005 ◽  
Vol 7 (3) ◽  
pp. e36 ◽  
Author(s):  
Crystale Purvis Cooper ◽  
Kenneth P Mallon ◽  
Steven Leadbetter ◽  
Lori A Pollack ◽  
Lucy A Peipins
Keyword(s):  
United States ◽  
Search Engine ◽  
Internet Search ◽  
Search Activity

Small investors’ internet sentiment and return predictability

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Antti Klemola
Keyword(s):  
Investor Sentiment ◽  
Equity Market ◽  
Individual Investors ◽  
Linear Regression Models ◽  
Internet Search ◽  
Search Activity ◽  
Market Returns ◽  
Content Type ◽  
Vector Autoregressive ◽  
Bear Market

Purpose The purpose of this paper is to propose a novel and new direct measurement of small investor sentiment in the equity market. The sentiment is based on the individual investors’ internet search activity. Design/methodology/approach The author measures unexpected changes in the small investor sentiment with AR (1) process, where the residuals capture the unexpected changes in small investor sentiment. The author employs vector autoregressive, Granger causality and linear regression models to estimate the association between the unexpected changes in small investor sentiment and future equity market returns. Findings An unexpected increase in the search popularity of the term bear market is negatively associated with the following week’s equity market returns. An unexpected increase in the spread (the difference in popularities between a bull market and a bear market) is positively associated with the following week’s equity market returns. The author finds that these effects are stronger for small-sized companies. Originality/value By author’s knowledge, the paper is the first that measures the small investor sentiment that is based on the internet search activity for keywords used in the American Association of Individual Investor’s (AAII) survey questions. The paper proposes an alternative small investor sentiment measure that captures the changes in small investor sentiment in more timely fashion than the AAII survey.

scholarly journals Erratum to: The effect of the metal-on-metal hip controversy on Internet search activity

2014 ◽  
Vol 24 (8) ◽  
pp. 1635-1635
Author(s):  
Nigel Phelan ◽  
John C. Kelly ◽  
David P. Moore ◽  
Patrick Kenny
Keyword(s):  
Internet Search ◽  
Search Activity ◽  
Metal On Metal

scholarly journals Effectivity of Awareness Months in Increasing Internet Search Activity for Top Malignancies Among Women

2017 ◽  
Vol 3 (3) ◽  
pp. e55 ◽  
Author(s):  
Dhruvika Mukhija ◽  
Anand Venkatraman ◽  
Sajan Jiv Singh Nagpal
Keyword(s):  
Internet Search ◽  
Search Activity

scholarly journals Internet search query data improve forecasts of daily emergency department volume

2019 ◽  
Vol 26 (12) ◽  
pp. 1574-1583 ◽  
Author(s):  
Sam Tideman ◽  
Mauricio Santillana ◽  
Jonathan Bickel ◽  
Ben Reis
Keyword(s):  
Predictive Power ◽  
Linear Models ◽  
Data Sources ◽  
Weather Data ◽  
Volume Data ◽  
Internet Search ◽  
Search Activity ◽  
Search Volume ◽  
Day Of The Week ◽  
Google Search

Abstract Objective Emergency departments (EDs) are increasingly overcrowded. Forecasting patient visit volume is challenging. Reliable and accurate forecasting strategies may help improve resource allocation and mitigate the effects of overcrowding. Patterns related to weather, day of the week, season, and holidays have been previously used to forecast ED visits. Internet search activity has proven useful for predicting disease trends and offers a new opportunity to improve ED visit forecasting. This study tests whether Google search data and relevant statistical methods can improve the accuracy of ED volume forecasting compared with traditional data sources. Materials and Methods Seven years of historical daily ED arrivals were collected from Boston Children’s Hospital. We used data from the public school calendar, National Oceanic and Atmospheric Administration, and Google Trends. Multiple linear models using LASSO (least absolute shrinkage and selection operator) for variable selection were created. The models were trained on 5 years of data and out-of-sample accuracy was judged using multiple error metrics on the final 2 years. Results All data sources added complementary predictive power. Our baseline day-of-the-week model recorded average percent errors of 10.99%. Autoregressive terms, calendar and weather data reduced errors to 7.71%. Search volume data reduced errors to 7.58% theoretically preventing 4 improperly staffed days. Discussion The predictive power provided by the search volume data may stem from the ability to capture population-level interaction with events, such as winter storms and infectious diseases, that traditional data sources alone miss. Conclusions This study demonstrates that search volume data can meaningfully improve forecasting of ED visit volume and could help improve quality and reduce cost.

scholarly journals Internet Search Activity of Young People With Mood Disorders Who Are Hospitalized for Suicidal Thoughts and Behaviors: Qualitative Study of Google Search Activity (Preprint)

2021 ◽  
Author(s):  
Khatiya C Moon ◽  
Anna R Van Meter ◽  
Michael A Kirschenbaum ◽  
Asra Ali ◽  
John M Kane ◽  
...  
Keyword(s):  
Young People ◽  
Behavioral Health ◽  
Mood Disorders ◽  
The Internet ◽  
Internet Search ◽  
Search Activity ◽  
Suicidal Thoughts ◽  
And Behaviors ◽  
Google Search ◽  
Suicidal Thoughts And Behaviors

BACKGROUND Little is known about the internet search activity of people with suicidal thoughts and behaviors (STBs). This data source has the potential to inform both clinical and public health efforts, such as suicide risk assessment and prevention. OBJECTIVE We aimed to evaluate the internet search activity of suicidal young people to find evidence of suicidal ideation and behavioral health–related content. METHODS Individuals aged between 15 and 30 years (N=43) with mood disorders who were hospitalized for STBs provided access to their internet search history. Searches that were conducted in the 3-month period prior to hospitalization were extracted and manually evaluated for search themes related to suicide and behavioral health. RESULTS A majority (27/43, 63%) of participants conducted suicide-related searches. Participants searched for information that exactly matched their planned or chosen method of attempting suicide in 21% (9/43) of cases. Suicide-related search queries also included unusual suicide methods and references to suicide in popular culture. A majority of participants (33/43, 77%) had queries related to help-seeking themes, including how to find inpatient and outpatient behavioral health care. Queries related to mood and anxiety symptoms were found among 44% (19/43) of participants and included references to panic disorder, the inability to focus, feelings of loneliness, and despair. Queries related to substance use were found among 44% (19/43) of participants. Queries related to traumatic experiences were present among 33% (14/43) of participants. Few participants conducted searches for crisis hotlines (n=3). CONCLUSIONS Individuals search the internet for information related to suicide prior to hospitalization for STBs. The improved understanding of the search activity of suicidal people could inform outreach, assessment, and intervention strategies for people at risk. Access to search data may also benefit the ongoing care of suicidal patients.

Sign in / Sign up

Export Citation Format

Share Document