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dc.contributor.authorMlawa, Filbert
dc.contributor.authorMkoba, Elizabeth
dc.contributor.authorMduma, Neema
dc.date.accessioned2024-06-06T11:53:58Z
dc.date.available2024-06-06T11:53:58Z
dc.date.issued2023-06-02
dc.identifier.urihttps://doi.org/10.48084/etasr.5636
dc.identifier.urihttps://dspace.nm-aist.ac.tz/handle/20.500.12479/2715
dc.descriptionThis research article was published by Engineering, Technology & Applied Science Research,Vol. 13 No,2023.en_US
dc.description.abstractThe 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.en_US
dc.language.isoenen_US
dc.publisherEngineering, Technology & Applied Science Researchen_US
dc.subjectCOVID-19 pandemicen_US
dc.subjectTwitteren_US
dc.subjectmisinformationen_US
dc.subjectmachine learningen_US
dc.subjectSwahili languageen_US
dc.titleA Machine Learning Model for detecting Covid-19 Misinformation in Swahili Languageen_US
dc.typeArticleen_US


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