Abstract
The COVID-19 pandemic brought upon a plethora of misinformation from fake news articles and posts on social media platforms. This necessitates the task of identifying whether a particular piece of information about COVID-19 is legitimate or not. However, with excessive misinformation spreading rapidly over the internet, manual verification of sources becomes infeasible. Several studies have already explored the use of machine learning towards automating COVID-19 misinformation detection. This paper will investigate COVID-19 misinformation detection in three parts. First, we identify the common themes found in COVID-19 misinformation data using Latent Dirichlet Allocation (LDA). Second, we use CatBoost as a classifier for detecting misinformation and compare its performance against other classifiers such as SVM, XGBoost, and LightGBM. Lastly, we highlight CatBoost’s most important features and decision-making mechanism using Shapley values.
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Avelino, J.N.M., Felizmenio Jr., E.P., Naval Jr., P.C. (2022). Unraveling COVID-19 Misinformation with Latent Dirichlet Allocation and CatBoost. In: Bădică, C., Treur, J., Benslimane, D., Hnatkowska, B., Krótkiewicz, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2022. Communications in Computer and Information Science, vol 1653. Springer, Cham. https://doi.org/10.1007/978-3-031-16210-7_2
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