  The 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 Naive
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 dataset.
