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A Study: Chikungunya Using Social Media Analytics in Delhi
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Author(s): Shivani Jain (Indira Gandhi Delhi Technical University for Women, India), Alankrita Aggarwal (Panipat Institute of Engineering and Technology, India)and Sandeep Mittal (Department of Mathematics, G. B. Pant Government Engineering College, India)
Copyright: 2021
Volume: 13
Issue: 1
Pages: 11
Source title:
International Journal of Security and Privacy in Pervasive Computing (IJSPPC)
Editor(s)-in-Chief: Tao Gao (Zionlion Group Ltd. Shanghai, China)
DOI: 10.4018/IJSPPC.2021010102
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Abstract
Chikungunya, an infection which is difficult to treat, took a toll on Delhi in year 2016. In that scenario, detection and prevention of vector-borne diseases outbreak in Delhi have been a major cause of concern for government. For analyzing this epidemic outbreak, the authors have utilized the unstructured data generated through Twitter. Twitter is a social media platform that generates vast amount of epidemic-related information every day. This information is used to analyze the effect of epidemic outbreak in Delhi region. In this paper, the authors discussed an associated study of various machine learning techniques for analyzing and mining social media information. In this, the authors have also categorized and explore the steps involved in social media textual data to provide a pictorial view of the ongoing outbreak. Finally, the article discussed the challenges faced for mining social media data.
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