  Disinformation through fake news is an ongoing problem in our society and has
become easily spread through social media. The most cost and time effective way
to filter these large amounts of data is to use a combination of human and
technical interventions to identify it. From a technical perspective, Natural
Language Processing (NLP) is widely used in detecting fake news. Social media
companies use NLP techniques to identify the fake news and warn their users,
but fake news may still slip through undetected. It is especially a problem in
more localised contexts (outside the United States of America). How do we
adjust fake news detection systems to work better for local contexts such as in
South Africa. In this work we investigate fake news detection on South African
websites. We curate a dataset of South African fake news and then train
detection models. We contrast this with using widely available fake news
datasets (from mostly USA website). We also explore making the datasets more
diverse by combining them and observe the differences in behaviour in writing
between nations' fake news using interpretable machine learning.
