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Reseach Article

Privacy and Security Issues: An Assessment of the Awareness Level of Smartphone Users in Nigeria

by Omeka Friday Odey, Joshua Abah, Dekera Kenneth Kwaghtyo
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 41
Year of Publication: 2023
Authors: Omeka Friday Odey, Joshua Abah, Dekera Kenneth Kwaghtyo

Omeka Friday Odey, Joshua Abah, Dekera Kenneth Kwaghtyo . Privacy and Security Issues: An Assessment of the Awareness Level of Smartphone Users in Nigeria. International Journal of Applied Information Systems. 12, 41 ( Oct 2023), 48-60. DOI=10.5120/ijais2023451948

@article{ 10.5120/ijais2023451948,
author = { Omeka Friday Odey, Joshua Abah, Dekera Kenneth Kwaghtyo },
title = { Privacy and Security Issues: An Assessment of the Awareness Level of Smartphone Users in Nigeria },
journal = { International Journal of Applied Information Systems },
issue_date = { Oct 2023 },
volume = { 12 },
number = { 41 },
month = { Oct },
year = { 2023 },
issn = { 2249-0868 },
pages = { 48-60 },
numpages = {9},
url = { },
doi = { 10.5120/ijais2023451948 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2023-10-20T21:10:07.180165+05:30
%A Omeka Friday Odey
%A Joshua Abah
%A Dekera Kenneth Kwaghtyo
%T Privacy and Security Issues: An Assessment of the Awareness Level of Smartphone Users in Nigeria
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 41
%P 48-60
%D 2023
%I Foundation of Computer Science (FCS), NY, USA

The proliferation of deliberately misleading information, commonly known as fake news, poses a significant challenge in shaping public opinions. This paper presents a cutting-edge methodology for effectively identifying and combating fake news by harnessing the power of ensemble learning techniques. Recognizing the widespread influence of fake news and its detrimental societal effects, there is an urgent need for robust and adaptable identification models. Existing approaches often suffer from biases and lack adaptability due to their reliance on single algorithms or limited datasets. To address these limitations, the study introduces an ensemble learning model that incorporates a diverse range of algorithms, enhancing accuracy and adaptability across various fake news contexts. Leveraging a benchmark dataset, the established model attained an exceptional accuracy rate of 97.86% using the test dataset, outperforming existing architectures. Through this research, the researchers aim to mitigate the adverse impact of fake news on social media platforms and provide a more reliable means of content verification.

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Index Terms

Computer Science
Information Sciences
News Classification
Text Recognition


Machine Learning – Fake News – Ensemble Learning – Identification Models – Social Media