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
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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.
UR - https://www.scopus.com/pages/publications/85202291930
U2 - 10.1007/978-3-031-64495-5_11
DO - 10.1007/978-3-031-64495-5_11
M3 - Conference contribution
AN - SCOPUS:85202291930
SN - 9783031644948
T3 - EAI/Springer Innovations in Communication and Computing
SP - 147
EP - 159
BT - 7th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2023
A2 - Gül, Ömer Melih
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 -