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Title: Real-time disease detection and analysis system using social media contents

Authors: SoYeop Yoo; DaeHo Kim; SungMin Yang; OkRan Jeong

Addresses: Department of Software, Gachon University, Seongnam-si, Gyeonggi-do, 13120, Korea ' Department of Software, Gachon University, Seongnam-si, Gyeonggi-do, 13120, Korea ' Department of Software, Gachon University, Seongnam-si, Gyeonggi-do, 13120, Korea ' Department of Software, Gachon University, Seongnam-si, Gyeonggi-do, 13120, Korea

Abstract: Nowadays, research that uses social media has become active. In particular, many studies have attempted to use social media as a sensor, because it generates information in real time, covering topics ranging from daily life to social issues such as disasters. In the area of public health, social media is also being used as a sensor for infectious disease surveillance. However, there are a number of challenges including the need to process efficiently large amounts of social media contents in real time and to detect diseases accurately. We have developed a system that detects disease in real-time and analyses using social media contents. We have built a workflow that enables real-time processing and developing a model managing data and detecting disease accurately. The system also provides useful information by analysing opinions and visualising information in real time. We have verified the effectiveness of the system.

Keywords: big data; real-time processing; disease detection; Apache AsterixDB; Apache Kafka; Google BERT.

DOI: 10.1504/IJWGS.2020.106103

International Journal of Web and Grid Services, 2020 Vol.16 No.1, pp.22 - 38

Received: 22 Feb 2019
Accepted: 07 Aug 2019

Published online: 25 Mar 2020 *

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