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An ontology-based framework for form...

Jayawardhana, Maddumage Udaya Kumara.

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An ontology-based framework for formulating spatio-temporal influenza (flu) outbreaks from Twitter.

Record Type:	Electronic resources : Monograph/item
Title/Author:	An ontology-based framework for formulating spatio-temporal influenza (flu) outbreaks from Twitter./
Author:	Jayawardhana, Maddumage Udaya Kumara.
Description:	50 p.
Notes:	Source: Masters Abstracts International, Volume: 56-01.
Contained By:	Masters Abstracts International56-01(E).
Subject:	Geographic information science and geodesy. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10145042
ISBN:	9781369006018

An ontology-based framework for formulating spatio-temporal influenza (flu) outbreaks from Twitter.
Jayawardhana, Maddumage Udaya Kumara.

An ontology-based framework for formulating spatio-temporal influenza (flu) outbreaks from Twitter. - 50 p.

Source: Masters Abstracts International, Volume: 56-01.

Thesis (M.S.)--Bowling Green State University, 2016.

Early detection and locating of influenza outbreaks is one of the key priorities on a national level for preparedness and planning. This study presents the design and implementation of a web-based prototype software framework (Fluwitter) for pseudo real-time detection of influenza outbreaks from Twitter in space and time. Harnessing social media to track real-time influenza outbreaks can provide different perspectives in battling the spread of infectious diseases and lowering the cost of existing assessment methods. Specifically, Fluwitter follows a three-tier architecture system with a thin web client and a resourceful server environment. The server side system is composed of a PostGIS spatial database, a GeoServer instance, a web application for visualizing influenza maps and daemon applications for tweet streaming, preprocessing of data, semantic information extraction based on DBpediaSpotlight and WS4J, and geo-processing. The collected geo-tagged tweets are processed by semantic NLP techniques for detecting and extracting influenza related tweets. The synsets from the extracted influenza related tweets are tagged and ontology based semantic similarity scores produced by WUP and RES algorithms were derived for subsequent information extraction. To ensure better detection, the information extraction was calibrated by different rules produced by the semantic similarity scores. The optimized rule produced a final F-measure value of 0.72 and accuracy (ACC) value of 94.4%. The Twitter generated influenza cases were validated by weekly influenza related hospitalization records issued by ODH. The validation that was based on Pearson's correlations suggested existence of moderate correlations for the Southeast region (r = 0.52), the Northwestern region (r = 0.38), and the Central region (r = 0.33). Although, additional work is needed, the potential strengths and benefits of the prototype are shown through a case study in Ohio that enables spatio-temporal assessment and visualization of influenza spread across the state.

ISBN: 9781369006018Subjects--Topical Terms:

2122917
Geographic information science and geodesy.

An ontology-based framework for formulating spatio-temporal influenza (flu) outbreaks from Twitter.
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245 1 3 $a An ontology-based framework for formulating spatio-temporal influenza (flu) outbreaks from Twitter.
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500 $a Source: Masters Abstracts International, Volume: 56-01.
500 $a Adviser: Peter Gorsevski,Dr.
502 $a Thesis (M.S.)--Bowling Green State University, 2016.
520 $a Early detection and locating of influenza outbreaks is one of the key priorities on a national level for preparedness and planning. This study presents the design and implementation of a web-based prototype software framework (Fluwitter) for pseudo real-time detection of influenza outbreaks from Twitter in space and time. Harnessing social media to track real-time influenza outbreaks can provide different perspectives in battling the spread of infectious diseases and lowering the cost of existing assessment methods. Specifically, Fluwitter follows a three-tier architecture system with a thin web client and a resourceful server environment. The server side system is composed of a PostGIS spatial database, a GeoServer instance, a web application for visualizing influenza maps and daemon applications for tweet streaming, preprocessing of data, semantic information extraction based on DBpediaSpotlight and WS4J, and geo-processing. The collected geo-tagged tweets are processed by semantic NLP techniques for detecting and extracting influenza related tweets. The synsets from the extracted influenza related tweets are tagged and ontology based semantic similarity scores produced by WUP and RES algorithms were derived for subsequent information extraction. To ensure better detection, the information extraction was calibrated by different rules produced by the semantic similarity scores. The optimized rule produced a final F-measure value of 0.72 and accuracy (ACC) value of 94.4%. The Twitter generated influenza cases were validated by weekly influenza related hospitalization records issued by ODH. The validation that was based on Pearson's correlations suggested existence of moderate correlations for the Southeast region (r = 0.52), the Northwestern region (r = 0.38), and the Central region (r = 0.33). Although, additional work is needed, the potential strengths and benefits of the prototype are shown through a case study in Ohio that enables spatio-temporal assessment and visualization of influenza spread across the state.
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