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Enhancing Fake News Identification in Social Media through Ensemble Learning Methods

by Timothy Moses, Henry Egaga Obi, Christopher Ifeanyi Eke, Jeffrey Agushaka
journal cover thumbnail

International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 41
Year of Publication: 2023
Authors: Timothy Moses, Henry Egaga Obi, Christopher Ifeanyi Eke, Jeffrey Agushaka
10.5120/ijais2023451949

Timothy Moses, Henry Egaga Obi, Christopher Ifeanyi Eke, Jeffrey Agushaka . Enhancing Fake News Identification in Social Media through Ensemble Learning Methods. International Journal of Applied Information Systems. 12, 41 ( Sep 2023), 1-22. DOI=10.5120/ijais2023451949

@article{ 10.5120/ijais2023451949,
author = { Timothy Moses, Henry Egaga Obi, Christopher Ifeanyi Eke, Jeffrey Agushaka },
title = { Enhancing Fake News Identification in Social Media through Ensemble Learning Methods },
journal = { International Journal of Applied Information Systems },
issue_date = { Sep 2023 },
volume = { 12 },
number = { 41 },
month = { Sep },
year = { 2023 },
issn = { 2249-0868 },
pages = { 1-22 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number41/enhancing-fake-news-identification-in-social-media-through-ensemble-learning-methods/ },
doi = { 10.5120/ijais2023451949 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-14T17:32:45.348415+05:30
%A Timothy Moses
%A Henry Egaga Obi
%A Christopher Ifeanyi Eke
%A Jeffrey Agushaka
%T Enhancing Fake News Identification in Social Media through Ensemble Learning Methods
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 41
%P 1-22
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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

Keywords

Machine Learning Fake News Ensemble Learning Identification Models Social Media

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