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Thông tin trách nhiệm: Kirill, Yakunin; Mihail, MihailIonescu George; Sanzhar, Murzakhmetov; Rustam, Mussabayev; Olga, Filatova; Ravil, Mukhamediev
Nhà Xuất Bản: Elsevier B.V
Năm Xuất Bản: 2020
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This paper presents a method based on topic modelling for identifying texts with propagandistic content. The method is an attempt to incorporate transfer learning idea of obtaining effective vector representation from a large unlabeled or (semi-) automatically labelled dataset, while also attempting to minimize the amount of necessary manual expert labelling by introducingrnhigh level labelling (either manual or automatic) on some explicit document property. The proposed method includes four key stages: formation of corpus partitioning, computing a topic model of a united corpus, calculation of corpora imbalance estimates of each topic; extrapolating the results of the imbalance estimation on all documents. The method was cross-validated on arnlabelled subsample of 1000 news, and achieves high predictive power – ROC AUC 0.73
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