Nabende, PeterKabiito, DavidBabirye, ClaireTusiime, HewittNakatumba-Nabende, Joyce2022-12-292022-12-292021Nabende, P., Kabiito, D., Babirye, C., Tusiime, H., & Nakatumba-Nabende, J. (2021). Misinformation detection in Luganda-English code-mixed social media text. arXiv preprint arXiv:2104.00124.https://arxiv.org/abs/2104.00124https://nru.uncst.go.ug/handle/123456789/6746The increasing occurrence, forms, and negative effects of misinformation on social media platforms has necessitated more misinformation detection tools. Currently, work is being done addressing COVID-19 misinformation however, there are no misinformation detection tools for any of the 40 distinct indigenous Ugandan languages. This paper addresses this gap by presenting basic language resources and a misinformation detection data set based on code-mixed Luganda- English messages sourced from the Facebook and Twitter social media platforms. Several machine learning methods are applied on the misinformation detection data set to develop classification models for detecting whether a code-mixed Luganda-English message contains misinformation or not. A 10-fold cross validation evaluation of the classification methods in an experimental misinformation detection task shows that a Discriminative Multinomial Na¨ıve Bayes (DMNB) method achieves the highest accuracy and F-measure of 78.19% and 77.90% respectively. Also, Support Vector Machine and Bagging ensemble classification models achieve comparable results. These results are promising since the machine learning models are based on n-gram features from only the misinformation detection data set.enOther