A Machine Learning Model for detecting Covid-19 Misinformation in Swahili Language
Received: 29 December 2022 | Revised: 2 February 2023 | Accepted: 5 February 2023 | Online: 2 June 2023
Corresponding author: Filbert Mlawa
Abstract
The recorded cases of corona virus (COVID-19) pandemic disease are millions and its mortality rate was maximized during the period from April 2020 to January 2022. Misinformation arose regarding this threat, which spread through social media platforms, and especially Twitter, often spreading confusion, social turmoil, and panic to the public. To identify such misinformation, a machine learning model is needed to detect whether the given information is true (true information) or not (misinformation). The aim of this paper is to present a machine-learning model for detecting COVID-19 misinformation in the Swahili language in tweets. The five machine learning algorithms that were trained for detecting Swahili language misinformation related to COVID-19 are Logistic Regression (LR), Support Vector Machine (SVM), Bagging Ensemble (BE), Multinomial Naïve Bayes (MNB), and Random Forest (RF). The study used the qualitative research method because non-numerical data, i.e. text, were used. Python programming language was used for data analysis due to its powerful libraries such as pandas and numpy. Four metrics were used to evaluate the model performance. The results revealed that SVM achieved the highest accuracy of 83.67% followed by LR with 82.47%. MNB achieved the best precision of 92.00% and in terms of recall and F1-score, RF, and SVM achieved the best results with 84.82% and 81.45%, respectively. This study will enable the public to easily identify Swahili language misinformation related to COVID-19 that is circulated on Twitter social media platform.
Keywords:
COVID-19 pandemic, Twitter, misinformation, machine learning, Swahili languageDownloads
References
A. Glazkova, "A Comparison of Text Representation Methods for Predicting Political Views of Social Media Users," in Proceedings of the International Scientific and Practical Conference "Information Technologies and Intelligent Decision Making Systems," Moscow, Russia, Jan. 2021, Art. no. 41.
M. N. Alenezi and Z. M. Alqenaei, "Machine Learning in Detecting COVID-19 Misinformation on Twitter," Future Internet, vol. 13, no. 10, Oct. 2021, Art. no. 244. DOI: https://doi.org/10.3390/fi13100244
S. Dadgar and M. Ghatee, "Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives." arXiv, Jul. 20, 2021.
B. Al-Ahmad, A. M. Al-Zoubi, R. Abu Khurma, and I. Aljarah, "An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information," Symmetry, vol. 13, no. 6, Jun. 2021, Art. no. 1091. DOI: https://doi.org/10.3390/sym13061091
A. Wani, I. Joshi, S. Khandve, V. Wagh, and R. Joshi, "Evaluating Deep Learning Approaches for Covid19 Fake News Detection," in Combating Online Hostile Posts in Regional Languages during Emergency Situation, 2021, pp. 153–163. DOI: https://doi.org/10.1007/978-3-030-73696-5_15
A. B. Shams et al., "Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic," Healthcare, vol. 9, no. 2, Feb. 2021, Art. no. 156. DOI: https://doi.org/10.3390/healthcare9020156
M. Bahremani, D. Berezovski, R. Perencsik, and Y. Zhang, "COVID-19 Fake News Detector," Sep. 2022, [Online]. Available: https://ssc.ca/sites/default/files/imce/second_prize.pdf.
Module 8: ‘False News’, Misinformation and Propaganda. Media Defence, 2020.
S. Alqurashi, B. Hamoui, A. Alashaikh, A. Alhindi, and E. Alanazi, "Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twitter." arXiv, Jan. 09, 2021.
P. Nabende, D. Kabiito, C. Babirye, H. Tusiime, and J. Nakatumba-Nabende, "Misinformation detection in Luganda-English code-mixed social media text." arXiv, Apr. 03, 2021.
M. K. Elhadad, K. F. Li, and F. Gebali, "Detecting Misleading Information on COVID-19," IEEE Access, vol. 8, pp. 165201–165215, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3022867
J. C. Medina Serrano, O. Papakyriakopoulos, and S. Hegelich, "NLP-based Feature Extraction for the Detection of COVID-19 Misinformation Videos on YouTube," in Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020, Apr. 2020.
G. S. Cheema, S. Hakimov, and R. Ewerth, "TIB’s Visual Analytics Group at MediaEval ’20: Detecting Fake News on Corona Virus and 5G Conspiracy." arXiv, Jan. 10, 2021.
S. Khan, S. Hakak, N. Deepa, B. Prabadevi, K. Dev, and S. Trelova, "Detecting COVID-19-Related Fake News Using Feature Extraction," Frontiers in Public Health, vol. 9, 2022. DOI: https://doi.org/10.3389/fpubh.2021.788074
M.-Y. Chen and Y.-W. Lai, "Using Fuzzy Clustering with Deep Learning Models for Detection of COVID-19 Disinformation," ACM Transactions on Asian and Low-Resource Language Information Processing, Apr. 2022. DOI: https://doi.org/10.1145/3548458
H. Alhakami, W. Alhakami, A. Baz, M. Faizan, M. W. Khan, and A. Agrawal, "Evaluating Intelligent Methods for Detecting COVID-19 Fake News on Social Media Platforms," Electronics, vol. 11, no. 15, Jan. 2022, Art. no. 2417. DOI: https://doi.org/10.3390/electronics11152417
S. Nuanmeesri, "A Hybrid Deep Learning and Optimized Machine Learning Approach for Rose Leaf Disease Classification," Engineering, Technology & Applied Science Research, vol. 11, no. 5, pp. 7678–7683, Oct. 2021. DOI: https://doi.org/10.48084/etasr.4455
M. A. Alsuwaiket, "Feature Extraction of EEG Signals for Seizure Detection Using Machine Learning Algorthims," Engineering, Technology & Applied Science Research, vol. 12, no. 5, pp. 9247–9251, Oct. 2022. DOI: https://doi.org/10.48084/etasr.5208
W. M. S. Yafooz, E. A. Hizam, and W. A. Alromema, "Arabic Sentiment Analysis on Chewing Khat Leaves using Machine Learning and Ensemble Methods," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 6845–6848, Apr. 2021. DOI: https://doi.org/10.48084/etasr.4026
M. Anwer, S. M. Khan, M. U. Farooq, and Waseemullah, "Attack Detection in IoT using Machine Learning," Engineering, Technology & Applied Science Research, vol. 11, no. 3, pp. 7273–7278, Jun. 2021. DOI: https://doi.org/10.48084/etasr.4202
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Copyright (c) 2023 Filbert Mlawa, Elizabeth Mkoba, Neema Mduma
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