Abstract
Online disinformation poses a growing threat, requiring fact-checking and detection/prevention measures. To address this, we propose a hierarchical classification approach using the DistilBERT and XLM-RoBERTa ensemble architectures on the Urdu Corpus of Disinformation (UCoD). Our ensemble outperforms other models like RNNs, LSTMs, k-nearest neighbors, random forests, and quadratic discriminant analysis, achieving a weighted F1 of 68.7 on UCoD. These results confirm the advantage of ensembles for imbalanced corpora, supporting the use of deep learning techniques in combating disinformation.
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Farooq, U., Beg, O., Riaz, F., Jamali, S., Holderbaum, W., Raza, U. (2024). UCoD: Ensemble BERT for Hierarchical Classification of the Urdu Disinformation Corpus. In: Ekpo, S.C. (eds) The Second International Adaptive and Sustainable Science, Engineering and Technology Conference. ASSET 2023. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-53935-0_25
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