Detection of Misinformation Related to Pandemic Diseases Using Machine Learning

Javaria Naeem; Ömer Melih Gül; Ismail Burak Parlak; Kostas Karpouzis; Seifedine Nimer Kadry; Yücel Batu Salman

doi:10.1007/978-3-031-64495-5_11

Detection of Misinformation Related to Pandemic Diseases Using Machine Learning

Javaria Naeem, Ömer Melih Gül, Ismail Burak Parlak, Kostas Karpouzis^*, Seifedine Nimer Kadry, Yücel Batu Salman

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

The advent of the COVID-19 pandemic has brought with it not only a global health crisis but also an infodemic characterized by the rampant spread of misinformation on social media platforms. In response to the urgent need for effective misinformation detection, this study presents a comprehensive approach harnessing machine learning and deep learning techniques, culminating in ensemble methods, to combat the proliferation of COVID-19 misinformation on Facebook, Twitter, Instagram, and YouTube. Drawing from a rich dataset comprising user comments on these platforms, encompassing diverse COVID-19-related discussions, our research applies SVM, decision tree, logistic regression, and neural networks to perform in-depth analysis and classification of comments into two categories: positive and negative information. The innovation of our approach lies in the final phase, where we employ ensemble methods to consolidate the strengths of various machine learning and deep learning algorithms. After applying ensemble learning, accuracy reached 91% for Facebook content, 79% for Instagram, 80% for Twitter, and 95% for YouTube.

Original language	English
Title of host publication	7th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2023
Editors	Ömer Melih Gül, Paolo Fiorini, Seifedine Nimer Kadry
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	147-159
Number of pages	13
ISBN (Print)	9783031644948
DOIs	https://doi.org/10.1007/978-3-031-64495-5_11
Publication status	Published - 2024
Externally published	Yes
Event	7th EAI International Conference on Robotics and Networks, ROSENET 2023 - Istanbul, Turkey Duration: 15 Dec 2023 → 16 Dec 2023

Publication series

Name	EAI/Springer Innovations in Communication and Computing
ISSN (Print)	2522-8595
ISSN (Electronic)	2522-8609

Conference

Conference	7th EAI International Conference on Robotics and Networks, ROSENET 2023
Country/Territory	Turkey
City	Istanbul
Period	15/12/23 → 16/12/23

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Access to Document

10.1007/978-3-031-64495-5_11

Cite this

Naeem, J., Gül, Ö. M., Parlak, I. B., Karpouzis, K., Kadry, S. N., & Salman, Y. B. (2024). Detection of Misinformation Related to Pandemic Diseases Using Machine Learning. In Ö. M. Gül, P. Fiorini, & S. N. Kadry (Eds.), 7th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2023 (pp. 147-159). (EAI/Springer Innovations in Communication and Computing). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-64495-5_11

Naeem, Javaria ; Gül, Ömer Melih ; Parlak, Ismail Burak et al. / Detection of Misinformation Related to Pandemic Diseases Using Machine Learning. 7th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2023. editor / Ömer Melih Gül ; Paolo Fiorini ; Seifedine Nimer Kadry. Springer Science and Business Media Deutschland GmbH, 2024. pp. 147-159 (EAI/Springer Innovations in Communication and Computing).

@inproceedings{0173205e13fe45eaaadb27cc1687d340,

title = "Detection of Misinformation Related to Pandemic Diseases Using Machine Learning",

abstract = "The advent of the COVID-19 pandemic has brought with it not only a global health crisis but also an infodemic characterized by the rampant spread of misinformation on social media platforms. In response to the urgent need for effective misinformation detection, this study presents a comprehensive approach harnessing machine learning and deep learning techniques, culminating in ensemble methods, to combat the proliferation of COVID-19 misinformation on Facebook, Twitter, Instagram, and YouTube. Drawing from a rich dataset comprising user comments on these platforms, encompassing diverse COVID-19-related discussions, our research applies SVM, decision tree, logistic regression, and neural networks to perform in-depth analysis and classification of comments into two categories: positive and negative information. The innovation of our approach lies in the final phase, where we employ ensemble methods to consolidate the strengths of various machine learning and deep learning algorithms. After applying ensemble learning, accuracy reached 91% for Facebook content, 79% for Instagram, 80% for Twitter, and 95% for YouTube.",

author = "Javaria Naeem and G{\"u}l, {{\"O}mer Melih} and Parlak, {Ismail Burak} and Kostas Karpouzis and Kadry, {Seifedine Nimer} and Salman, {Y{\"u}cel Batu}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 7th EAI International Conference on Robotics and Networks, ROSENET 2023 ; Conference date: 15-12-2023 Through 16-12-2023",

year = "2024",

doi = "10.1007/978-3-031-64495-5_11",

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}

Naeem, J, Gül, ÖM, Parlak, IB, Karpouzis, K, Kadry, SN & Salman, YB 2024, Detection of Misinformation Related to Pandemic Diseases Using Machine Learning. in ÖM Gül, P Fiorini & SN Kadry (eds), 7th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2023. EAI/Springer Innovations in Communication and Computing, Springer Science and Business Media Deutschland GmbH, pp. 147-159, 7th EAI International Conference on Robotics and Networks, ROSENET 2023, Istanbul, Turkey, 15/12/23. https://doi.org/10.1007/978-3-031-64495-5_11

Detection of Misinformation Related to Pandemic Diseases Using Machine Learning. / Naeem, Javaria; Gül, Ömer Melih; Parlak, Ismail Burak et al.
7th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2023. ed. / Ömer Melih Gül; Paolo Fiorini; Seifedine Nimer Kadry. Springer Science and Business Media Deutschland GmbH, 2024. p. 147-159 (EAI/Springer Innovations in Communication and Computing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Detection of Misinformation Related to Pandemic Diseases Using Machine Learning

AU - Naeem, Javaria

AU - Gül, Ömer Melih

AU - Parlak, Ismail Burak

AU - Karpouzis, Kostas

AU - Kadry, Seifedine Nimer

AU - Salman, Yücel Batu

PY - 2024

Y1 - 2024

N2 - The advent of the COVID-19 pandemic has brought with it not only a global health crisis but also an infodemic characterized by the rampant spread of misinformation on social media platforms. In response to the urgent need for effective misinformation detection, this study presents a comprehensive approach harnessing machine learning and deep learning techniques, culminating in ensemble methods, to combat the proliferation of COVID-19 misinformation on Facebook, Twitter, Instagram, and YouTube. Drawing from a rich dataset comprising user comments on these platforms, encompassing diverse COVID-19-related discussions, our research applies SVM, decision tree, logistic regression, and neural networks to perform in-depth analysis and classification of comments into two categories: positive and negative information. The innovation of our approach lies in the final phase, where we employ ensemble methods to consolidate the strengths of various machine learning and deep learning algorithms. After applying ensemble learning, accuracy reached 91% for Facebook content, 79% for Instagram, 80% for Twitter, and 95% for YouTube.

AB - The advent of the COVID-19 pandemic has brought with it not only a global health crisis but also an infodemic characterized by the rampant spread of misinformation on social media platforms. In response to the urgent need for effective misinformation detection, this study presents a comprehensive approach harnessing machine learning and deep learning techniques, culminating in ensemble methods, to combat the proliferation of COVID-19 misinformation on Facebook, Twitter, Instagram, and YouTube. Drawing from a rich dataset comprising user comments on these platforms, encompassing diverse COVID-19-related discussions, our research applies SVM, decision tree, logistic regression, and neural networks to perform in-depth analysis and classification of comments into two categories: positive and negative information. The innovation of our approach lies in the final phase, where we employ ensemble methods to consolidate the strengths of various machine learning and deep learning algorithms. After applying ensemble learning, accuracy reached 91% for Facebook content, 79% for Instagram, 80% for Twitter, and 95% for YouTube.

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T3 - EAI/Springer Innovations in Communication and Computing

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BT - 7th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2023

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A2 - Fiorini, Paolo

A2 - Kadry, Seifedine Nimer

PB - Springer Science and Business Media Deutschland GmbH

T2 - 7th EAI International Conference on Robotics and Networks, ROSENET 2023

Y2 - 15 December 2023 through 16 December 2023

ER -

Naeem J, Gül ÖM, Parlak IB, Karpouzis K, Kadry SN, Salman YB. Detection of Misinformation Related to Pandemic Diseases Using Machine Learning. In Gül ÖM, Fiorini P, Kadry SN, editors, 7th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2023. Springer Science and Business Media Deutschland GmbH. 2024. p. 147-159. (EAI/Springer Innovations in Communication and Computing). doi: 10.1007/978-3-031-64495-5_11

Detection of Misinformation Related to Pandemic Diseases Using Machine Learning

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