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DOI: 10.14569/IJACSA.2020.0111028
PDF

Tracking Coronavirus Pandemic Diseases using Social Media: A Machine Learning Approach

Author 1: Nuha Noha Fakhry
Author 2: Evan Asfoura
Author 3: Gamal Kassam

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 10, 2020.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: With the increasing use of social media, a growing need exists for systems that can extract useful information from huge amounts of data. While, People post personal data interactively, an outbreak of an epidemic event can be noticed from these data. The issue of detecting the route of pandemic diseases is addressed. The main objective of this research work is to use a dual machine learning approach to evaluate current and future data of Covid-19 cases based on published social media information in specific geographical region and show how the disease spreads geographically over the time. The dual machine learning approach used based on traditional data mining methods to estimate disease cases found in social media related to specific geographical region. On other hand, sentiment analysis is conducted to assess the public perception of the disease awareness on the same region.

Keywords: Pandemic diseases; outbreak detection; social media; sentiment analysis; machine learning; text mining; geo-located data; CRISP-DM

Nuha Noha Fakhry, Evan Asfoura and Gamal Kassam, “Tracking Coronavirus Pandemic Diseases using Social Media: A Machine Learning Approach” International Journal of Advanced Computer Science and Applications(IJACSA), 11(10), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111028

@article{Fakhry2020,
title = {Tracking Coronavirus Pandemic Diseases using Social Media: A Machine Learning Approach},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111028},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111028},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {10},
author = {Nuha Noha Fakhry and Evan Asfoura and Gamal Kassam}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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