  The recent advances in Natural Language Processing have only been a boon for
well represented languages, negating research in lesser known global languages.
This is in part due to the availability of curated data and research resources.
One of the current challenges concerning low-resourced languages are clear
guidelines on the collection, curation and preparation of datasets for
different use-cases. In this work, we take on the task of creating two datasets
that are focused on news headlines (i.e short text) for Setswana and Sepedi and
the creation of a news topic classification task from these datasets. In this
study, we document our work, propose baselines for classification, and
investigate an approach on data augmentation better suited to low-resourced
languages in order to improve the performance of the classifiers.
