• Corpus ID: 232478659

Misinformation detection in Luganda-English code-mixed social media text

@article{Nabende2021MisinformationDI,
  title={Misinformation detection in Luganda-English code-mixed social media text},
  author={Peter Nabende and David Kabiito and Claire Babirye and Hewitt Tusiime and Joyce Nakatumba-Nabende},
  journal={ArXiv},
  year={2021},
  volume={abs/2104.00124}
}

• Peter Nabende, David Kabiito, +2 authors J. Nakatumba-Nabende
• Published 31 March 2021
• Computer Science
• ArXiv

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.