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COVFake: A Word Embedding Coupled with LSTM Approach for COVID Related Fake News Detection

by Muhammad Usama Islam, Md. Mobarak Hossain, Mohammod Abul Kashem
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 174 - Number 10
Year of Publication: 2021
Authors: Muhammad Usama Islam, Md. Mobarak Hossain, Mohammod Abul Kashem
10.5120/ijca2021920977

Muhammad Usama Islam, Md. Mobarak Hossain, Mohammod Abul Kashem . COVFake: A Word Embedding Coupled with LSTM Approach for COVID Related Fake News Detection. International Journal of Computer Applications. 174, 10 ( Jan 2021), 1-5. DOI=10.5120/ijca2021920977

@article{ 10.5120/ijca2021920977,
author = { Muhammad Usama Islam, Md. Mobarak Hossain, Mohammod Abul Kashem },
title = { COVFake: A Word Embedding Coupled with LSTM Approach for COVID Related Fake News Detection },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2021 },
volume = { 174 },
number = { 10 },
month = { Jan },
year = { 2021 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume174/number10/31711-2021920977/ },
doi = { 10.5120/ijca2021920977 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:23:46.227837+05:30
%A Muhammad Usama Islam
%A Md. Mobarak Hossain
%A Mohammod Abul Kashem
%T COVFake: A Word Embedding Coupled with LSTM Approach for COVID Related Fake News Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 174
%N 10
%P 1-5
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Coronavirus (COVID) took a substantial toll on human life with its unprecedented arrival in human sphere. An unforeseen circumstance which lead to various types of guidelines of procedures directed from the monitoring bodies including face-mask guideline, hand-wash guidelines and so forth. However, with the advent of this disease, misinformation became a causal factor to this scenario albeit claiming millions of life in the process. A threatening disease coupled with misinformation has created a disastrous scenario in human life. Our approach, exploits the power of natural language processing, specifically word embedding and Long short term memory (LSTM) to detect the COVID related fake news. Our model performs with a promising accuracy of 96% which concludes our effort of contribution to this massive outbreak from a linguistic standpoint.

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Index Terms

Computer Science
Information Sciences

Keywords

Coronavirus news Fake news Natural language processing text analysis Long short term memory Word embedding Fake news detection

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