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Recent Advances in Computer Science and Communications


ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Research Article

A Deep Learning Model to Detect Fake News about COVID-19

Author(s): Selva Birunda Shanmugavel*, Kanniga Devi Rangaswamy and Muthiah Muthukannan

Volume 16, Issue 9, 2023

Published on: 21 September, 2023

Article ID: e250823220331 Pages: 9

DOI: 10.2174/2666255816666230825100307


Aims/Background: Twitter has rapidly become a go-to source for current events coverage. The more people rely on it, the more important it is to provide accurate data. Twitter makes it easy to spread misinformation, which can have a significant impact on how people feel, especially if false information spreads around COVID-19.

Methodology: Unfortunately, twitter was also used to spread myths and misinformation about the illness and its preventative immunization. So, it is crucial to identify false information before its spread gets out of hand. In this research, we look into the efficacy of several different types of deep neural networks in automatically classifying and identifying fake news content posted on social media platforms in relation to the COVID-19 pandemic. These networks include long short-term memory (LSTM), bi-directional LSTM, convolutional-neural-networks (CNN), and a hybrid of CNN-LSTM networks.

Results: The "COVID-19 Fake News" dataset includes 42,280, actual and fake news cases for the COVID-19 pandemic and associated vaccines and has been used to train and test these deep neural networks.

Conclusion: The proposed models are executed and compared to other deep neural networks, the CNN model was found to have the highest accuracy at 95.6%.

Keywords: Fake news detection, COVID-19, deep-learning, LSTM, BiLSTM, CNN.

Graphical Abstract
P. Dhiman, A. Kaur, C. Iwendi, and S.K. Mohan, "A scientometric analysis of deep learning approaches for detecting fake news", Electronics, vol. 12, no. 4, p. 948, 2023.
F. Fifita, J. Smith, M.B. Hanzsek-Brill, X. Li, and M. Zhou, "Machine learning-based identifications of COVID-19 fake news using biomedical information extraction", Big Data and Cognitive Computing, vol. 7, no. 1, p. 46, 2023.
L. Liu, M. Shafiq, V.R. Sonawane, M.Y.B. Murthy, P.C.S. Reddy, and K.M.N.C. Reddy, "Spectrum trading and sharing in unmanned aerial vehicles based on distributed blockchain consortium system", Comput. Electr. Eng., vol. 103, p. 108255, 2022.
A. Jarrahi, and L. Safari, "Evaluating the effectiveness of publishers’ features in fake news detection on social media", Multimedia Tools Appl., vol. 82, no. 2, pp. 2913-2939, 2023.
[] [PMID: 35431607]
L. Sujihelen, R. Boddu, S. Murugaveni, M. Arnika, A. Haldorai, P.C.S. Reddy, S. Feng, and J. Qin, "Node replication attack detection in distributed wireless sensor networks", Wirel. Commun. Mob. Comput., vol. 2022, pp. 1-11, 2022.
P.C.S. Reddy, G. Suryanarayana, and S. Yadala, "Data analytics in farming: Rice price prediction in andhra pradesh", 2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT), vol. 2022. 2022, pp. 1-5.
V. Balakrishnan, W.Z. Ng, M.C. Soo, G.J. Han, and C.J. Lee, "Infodemic and fake news – A comprehensive overview of its global magnitude during the COVID-19 pandemic in 2021: A scoping review", Int. J. Disaster Risk Reduct., vol. 78, p. 103144, 2022.
[] [PMID: 35791376]
A. Singhal, S. Varshney, T.A. Mohanaprakash, R. Jayavadivel, K. Deepti, P.C.S. Reddy, and M.B. Mulat, "Minimization of latency using multitask scheduling in industrial autonomous systems", Wirel. Commun. Mob. Comput., vol. 2022, pp. 1-10, 2022.
D. Balamurugan, S.S. Aravinth, P.C.S. Reddy, A. Rupani, and A. Manikandan, "Multiview objects recognition using deep learning-based wrap-CNN with voting scheme", Neural Process. Lett., vol. 54, no. 3, pp. 1495-1521, 2022.
P.C. Shaker Reddy, and A. Sureshbabu, "An enhanced multiple linear regression model for seasonal rainfall prediction", Int. J. Sensors Wirel. Commun. Control, vol. 10, no. 4, pp. 473-483, 2020.
Y. Sucharitha, Y. Vijayalata, and V.K. Prasad, "Predicting election results from twitter using machine learning algorithms", Recent Adv. Comput, vol. 14, no. 1, pp. 246-256, 2021.
R. Sabitha, A.P. Shukla, A. Mehbodniya, and L. Shakkeera, "A fuzzy trust evaluation of cloud collaboration outlier detection in wireless sensor networks", Ad Hoc Sens. Wirel. Netw., vol. 53, no. 3, p. 53, 2022.
K.A. Muthappa, A.S.A. Nisha, R. Shastri, V. Avasthi, and P.C.S. Reddy, "Design of high-speed, low-power non-volatile master slave flip flop (NVMSFF) for memory registers designs", Appl. Nanosci., vol. 13, no. 8, pp. 1-10, 2023.
B. Palani, "S. Elango, and V. Viswanathan K, CB-Fake: A multimodal deep learning framework for automatic fake news detection using capsule neural network and BERT", Multimedia Tools Appl., vol. 81, no. 4, pp. 5587-5620, 2022.
[] [PMID: 34975284]
S. Reddy, P. Chandra, and S. Yadala, "IoT-enabled energy-efficient multipath power control for underwater sensor networks", Int. J. Sensors Wirel. Commun. Control, vol. 12, no. 6, 2022.
S.D. Das, A. Basak, and S. Dutta, "A heuristic-driven uncertainty based ensemble framework for fake news detection in tweets and news articles", Neurocomputing, vol. 491, pp. 607-620, 2022.
Y. Long, Q. Lu, R. Xiang, M. Li, and C.R. Huang, "Fake news detection through multi-perspective speaker profiles", In Proceedings of the eighth international joint conference on natural language processing, vol. 2, 2017, pp. 252-256
F. Altunbey Ozbay, and B. Alatas, "A novel approach for detection of fake news on social media using metaheuristic optimization algorithms", Elektron. Elektrotech., vol. 25, no. 4, pp. 62-67, 2019.
Y. Fang, J. Gao, C. Huang, H. Peng, and R. Wu, "Self multi-head attention-based convolutional neural networks for fake news detection", PLoS One, vol. 14, no. 9, p. e0222713, 2019.
[] [PMID: 31557213]
P.C.S. Reddy, Y. Sucharitha, and G.S. Narayana, "Forecasting of Covid-19 virus spread using machine learning algorithm", Int. Biol. Biomed, vol. 6, 2021.
"A.M.P. Braşoveanu, and R. Andonie, Integrating machine learning techniques in semantic fake news detection", Neural Process. Lett., vol. 53, no. 5, pp. 3055-3072, 2021.
T.C. Truong, Q.B. Diep, I. Zelinka, and R. Senkerik, "Supervised classification methods for fake news identification", Artificial Intelligence and Soft Computing: 19th International Conference ICAISC 2020, vol. 19. 2020, no. Part II, pp. 445-454. Zakopane, Poland
S. Hakak, M. Alazab, S. Khan, T.R. Gadekallu, P.K.R. Maddikunta, and W.Z. Khan, "An ensemble machine learning approach through effective feature extraction to classify fake news", Future Gener. Comput. Syst., vol. 117, pp. 47-58, 2021.
P. Reddy, and A. Sureshbabu, "An adaptive model for forecasting seasonal rainfall using predictive analytics", International Journal of Intelligent Engineering and Systems, vol. 12, no. 5, pp. 22-32, 2019.
R. Thilagavathy, P.N. Renjith, R.V.S. Lalitha, M.Y.B. Murthy, Y. Sucharitha, and S.L. Narayanan, "A novel framework paradigm for EMR management cloud system authentication using blockchain security network", Soft Comput., pp. 1-9, 2023.
S. Ghannay, B. Favre, Y. Esteve, and N. Camelin, "Word embedding evaluation and combination", In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), 2016, pp. 300-305
Y. Tashtoush, B. Alrababah, O. Darwish, M. Maabreh, and N. Alsaedi, "A deep learning framework for detection of covid-19 fake news on social media platforms", Data, vol. 7, no. 5, p. 65, 2022.
P. Afshar, S. Heidarian, F. Naderkhani, A. Oikonomou, K.N. Plataniotis, and A. Mohammadi, "COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images", Pattern Recognit. Lett., vol. 138, pp. 638-643, 2020.
[] [PMID: 32958971]
A.S.R. Srinivasa Rao, and J.A. Vazquez, "Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone–based survey when cities and towns are under quarantine", Infect. Control Hosp. Epidemiol., vol. 41, no. 7, pp. 826-830, 2020.
[] [PMID: 32122430]
M. Rostami, and M. Oussalah, "A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest", Informatics in Medicine Unlocked, vol. 30, p. 100941, 2022.
[] [PMID: 35399333]
M.J. Umer, J. Amin, M. Sharif, M.A. Anjum, F. Azam, and J.H. Shah, "An integrated framework for COVID-19 classification based on classical and quantum transfer learning from a chest radiograph", Concurr. Comput., vol. 34, no. 20, p. e6434, 2022.
[] [PMID: 34512201]
S. Heidarian, P. Afshar, N. Enshaei, F. Naderkhani, M.J. Rafiee, F. Babaki Fard, K. Samimi, S.F. Atashzar, A. Oikonomou, K.N. Plataniotis, and A. Mohammadi, "Covid-fact: A fully-automated capsule network-based framework for identification of covid-19 cases from chest ct scans", Frontiers in Artificial Intelligence, vol. 4, p. 598932, 2021.
[] [PMID: 34113843]

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