Language Independent Models for COVID-19 Fake News Detection Black Box versus White Box Models
Main Article Content
Keywords
Fake news, black box model, white box model, machine learning, COVID-19
Abstract
In an era where massive information can be spread easily through social media, fake news detention is increasingly used to prevent widespread misinformation, especially fake news regarding COVID-19. Databases have been built and machine-learning algorithms have been used to identify patterns in news content and filter the false information. A brief overview, ranging from public domain datasets through the deployment of several machine learning models, as well as feature extraction methods, is provided in this paper. As a case study, a mixed language dataset is presented. The dataset consists of tweets of COVID-19 which have been labelled as fake or real news. To perform the detection task, a classification model is implemented using language-independent features. In particular, the features offer numerical inputs that are invariant to the language type; thus, they are suitable for investigation, as many regions in the world have similar linguistic structures. Furthermore, the classification task can be performed by using black box or white box models, each having its own advantages and disadvantages. In this paper, we compare the performance of the two approaches. Simulation results show that the performance difference between black box models and white box models is not significant.
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References
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Hande, A., Puranik, K., Priyadharshini, R., Thavareesan, S., & Chakravarthi, B. R. (2021). Evaluating Pretrained Transformer-based Models for COVID-19 Fake News Detection. 5th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 766–772). https://doi.org/10.1109/ICCMC51019.2021.9418446
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Sep 30, 2023
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Wong, W. K., Juwono, F. H., Chew, I. M., & Lease, B. A. (2023). Language Independent Models for COVID-19 Fake News Detection: Black Box versus White Box Models. Journal of Telecommunications and the Digital Economy, 11(3), 84–104. https://doi.org/10.18080/jtde.v11n3.789
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Author Biographies
W. K. Wong, Sensor Research Group, Curtin University Malaysia, Miri, Malaysia
W. K. Wong is currently serving as Associate Professor with Department of Electrical and Computer Engineering, Curtin University Malaysia. He obtained his M. Eng. and Ph.D. from Universiti Malaysia Sabah in 2012 and 2016, respectively. Prior to joining academia, he was with the telecommunication and building services industry. His research interests include embedded system development, machine learning applications, and image processing. He is currently affiliated with Sensor Research Group, Curtin University Malaysia. He also functions as an associate editor for various journals and conferences.
Filbert H. Juwono, Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China
Filbert H. JUWONO received the B. Eng. degree in electrical engineering and the M. Eng. degree in telecommunication engineering from the University of Indonesia, Depok, Indonesia, in 2007 and 2009, respectively, and the Ph.D. degree in electrical and electronic engineering from The University of Western Australia, Perth, WA, Australia, in 2017. He is currently with Xiʼan Jiaotong-Liverpool University. His research interests include signal processing for communications, wireless communications, powerline communications, machine learning applications, and biomedical engineering. He was a recipient of the prestigious Australian Awards Scholarship, in 2012. He serves as an Associate Editor for IEEE Access, a Review Editor for Frontiers in Signal Processing, and the Editor in-Chief for a newly established journal Green Intelligent Systems and Applications. Senior Member, IEEE.
Ing Ming Chew, Department of Electrical and Computer Engineering, Curtin University Malaysia, Miri, Malaysia
Chew Ing Ming received the Bachelor of Engineering Degree in Electrical Engineering (major in instrumentation and control) from the University Tun Hussein Onn, Batu Pahat, Johor, Malaysia, in 2002 and the Ph.D. degree in electrical and electronics engineering from University Malaysia Sabah, Sabah, Malaysia, in 2020. He is currently a lecturer at Curtin University Malaysia in Miri, Sarawak, Malaysia. Since 2020, he served as the program coordinator for the Bachelor of Electrical and Electronics Engineering, Electrical and Computer Department from Curtin University Malaysia. His research interest includes optimization, PID Control and embedded systems IoT.
