Abstract
The COVID-19 pandemic has recently shed light on the potential for social media as a means of spreading mis-, dis-, and malinformation. This paper investigates embedding and cluster-based topic modelling to characterise the COVID-19 infodemic on South African Twitter, which has largely remained unstudied during the COVID-19 pandemic. The best performing model is able to identify specific misinformation narratives, but these narratives are mostly found within more general topics. A more fine-grained model is trained, and is able to much better isolate rumour/misinformation topics from more general topics. Finally, the paper makes several suggestions for dealing with the multilingual and code-switched nature of South African Twitter, as well as for the exploration and development of new dynamic topic modeling approaches that could be especially valuable for tracing the development of specific misinformation or rumour narratives over time. The paper presents novel insights and results on the application of a combination of data mining, machine learning and optimisation for addressing the pressing issue of misleading information on social media.
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This work is based on the research supported in part by the National Research Foundation of South Africa (Grant number: 129340).
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Strydom, I.F., Grobler, J. (2023). Topic Modelling for Characterizing COVID-19 Misinformation on Twitter: A South African Case Study. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023. ICCSA 2023. Lecture Notes in Computer Science, vol 13957. Springer, Cham. https://doi.org/10.1007/978-3-031-36808-0_19
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