Difference between revisions of "Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: a Comparative Analysis"

From Wikipedia Quality
Jump to: navigation, search
(Adding embed)
(+ categories)
 
Line 32: Line 32:
 
</nowiki>
 
</nowiki>
 
</code>
 
</code>
 +
 +
 +
 +
[[Category:Scientific works]]
 +
[[Category:Twi Wikipedia]]

Latest revision as of 08:23, 14 August 2020


Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: a Comparative Analysis
Authors
J Danielle Sharpe
Publication date
2016
DOI
10.2196/publichealth.5901
Links
Original

Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: a Comparative Analysis - scientific work related to Wikipedia quality published in 2016, written by J Danielle Sharpe.

Overview

Background: Traditional influenza surveillance relies on influenza-like illness (ILI) syndrome that is reported by health care providers. It primarily captures individuals who seek medical care and misses those who do not. Recently, Web-based data sources have been studied for application to public health surveillance, as there is a growing number of people who search, post, and tweet about their illnesses before seeking medical care. Existing research has shown some promise of using data from Google, Twitter, and Wikipedia to complement traditional surveillance for ILI. However, past studies have evaluated these Web-based sources individually or dually without comparing all 3 of them, and it would be beneficial to know which of the Web-based sources performs best in order to be considered to complement traditional methods. Objective: The objective of this study is to comparatively analyze Google, Twitter, and Wikipedia by examining which best corresponds with Centers for Disease Control and Prevention (CDC) ILI data. It was hypothesized that Wikipedia will best correspond with CDC ILI data as previous research found it to be least influenced by high media coverage in comparison with Google and Twitter. Methods: Publicly available, deidentified data were collected from the CDC, Google Flu Trends, HealthTweets, and Wikipedia for the 2012-2015 influenza seasons. Bayesian change point analysis was used to detect seasonal changes, or change points, in each of the data sources. Change points in Google, Twitter, and Wikipedia that occurred during the exact week, 1 preceding week, or 1 week after the CDC’s change points were compared with the CDC data as the gold standard. All analyses were conducted using the R package “bcp” version 4.0.0 in RStudio version 0.99.484 (RStudio Inc). In addition, sensitivity and positive predictive values (PPV) were calculated for Google, Twitter, and Wikipedia. Results: During the 2012-2015 influenza seasons, a high sensitivity of 92% was found for Google, whereas the PPV for Google was 85%. A low sensitivity of 50% was calculated for Twitter; a low PPV of 43% was found for Twitter also. Wikipedia had the lowest sensitivity of 33% and lowest PPV of 40%. Conclusions: Of the 3 Web-based sources, Google had the best combination of sensitivity and PPV in detecting Bayesian change points in influenza-related data streams. Findings demonstrated that change points in Google, Twitter, and Wikipedia data occasionally aligned well with change points captured in CDC ILI data, yet these sources did not detect all changes in CDC data and should be further studied and developed. [JMIR Public Health Surveill 2016;2(2):e161]

Embed

Wikipedia Quality

Sharpe, J Danielle. (2016). "[[Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: a Comparative Analysis]]". JMIR Publications Inc., Toronto, Canada. DOI: 10.2196/publichealth.5901.

English Wikipedia

{{cite journal |last1=Sharpe |first1=J Danielle |title=Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: a Comparative Analysis |date=2016 |doi=10.2196/publichealth.5901 |url=https://wikipediaquality.com/wiki/Evaluating_Google,_Twitter,_and_Wikipedia_as_Tools_for_Influenza_Surveillance_Using_Bayesian_Change_Point_Analysis:_a_Comparative_Analysis |journal=JMIR Publications Inc., Toronto, Canada}}

HTML

Sharpe, J Danielle. (2016). &quot;<a href="https://wikipediaquality.com/wiki/Evaluating_Google,_Twitter,_and_Wikipedia_as_Tools_for_Influenza_Surveillance_Using_Bayesian_Change_Point_Analysis:_a_Comparative_Analysis">Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: a Comparative Analysis</a>&quot;. JMIR Publications Inc., Toronto, Canada. DOI: 10.2196/publichealth.5901.