Developing an early warning system of suicide using Google Trends and media reporting

Chai, Yi, Hao, Luo, Qingpeng, Zhang, Qijin, Cheng, Lui, Carrie, and Yip, Paul (2019) Developing an early warning system of suicide using Google Trends and media reporting. Journal of Affective Disorders, 255. pp. 41-49.

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Abstract

Background Conventional surveillance systems for suicides typically suffer from a substantial time lag of six months to two years. This study aims to develop an early warning system of possible suicide outbreaks in Hong Kong using Google Trends and suicide-related media reporting.

Methods Data on 3,534 suicides from 2011 to 2015 were obtained from Hong Kong Census and Statistics Department, and the Coroner's Court. Using data from Google Trends and features extracted from media reporting on suicide news, we fitted Poisson regression models to predict the number and estimate the intensity of suicides on a weekly basis, for six subgroups, defined by gender and age. We adopted the cumulative sum (CUSUM) control chart-based method to identify outbreaks of suicide.

Results The proposed model was able to predict the number of suicides with reasonably low normalized root mean squared errors, ranging from 15.6% for young females to 24.16% for old females. The suicide intensity curves were well captured by the proposed models for young males and females, but not for other groups. The Sensitivity, Precision and F1 Score of the CUSUM-based method were 50%, 100% and 67% for young females, and 93%, 54% and 68% for young males.

Limitations This study focused only on predicting the number of suicides in the current week, not in the future weeks. The model did not include social media, socioeconomic and climate data.

Conclusions Our results indicate that Google Trends search terms and media reporting data may be valuable data sources for predicting possible outbreak of suicides in Hong Kong. The proposed system could support effective and targeted interventions.

Item ID: 58469
Item Type: Article (Research - C1)
ISSN: 1573-2517
Keywords: Hong Kong; suicide prediction; Google Trends; media reporting; suicide news; early warning system
Copyright Information: © 2019 Elsevier B.V. All rights reserved.
Funders: Hong Kong SAR Government, Hong Kong General Research Fund, National Science Foundation of China (NSFC)
Date Deposited: 31 Jul 2019 01:27
FoR Codes: 11 MEDICAL AND HEALTH SCIENCES > 1117 Public Health and Health Services > 111711 Health Information Systems (incl Surveillance) @ 70%
11 MEDICAL AND HEALTH SCIENCES > 1117 Public Health and Health Services > 111799 Public Health and Health Services not elsewhere classified @ 30%
SEO Codes: 92 HEALTH > 9204 Public Health (excl. Specific Population Health) > 920410 Mental Health @ 50%
92 HEALTH > 9204 Public Health (excl. Specific Population Health) > 920404 Disease Distribution and Transmission (incl. Surveillance and Response) @ 50%
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