Is it Fake? News Disinformation Detection on South African News Websites
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 28 references that are in the map. First posted on arXiv 6 Aug 2021.
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8 of 28 references.
References · 8
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
- 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,416
- 2017"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detectioncited by 363
- 2017Fake News Detection on Social Media: A Data Mining Perspectivecited by 336
- 2018Techniques for Interpretable Machine Learningcited by 238
- 2017Automatic Detection of Fake Newscited by 145
- 2019Improving short text classification through global augmentation methodscited by 33
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