TF-IDF-BASED FAKE NEWS DETECTION IN KAZAKH AND RUSSIAN
DOI:
https://doi.org/10.53002/138Ключевые слова:
fake news, disinformation, Kazakh language, Russian language, TF-IDF, machine learning, cross-lingual classification.Аннотация
Automatic fake news detection has become an important applied problem in natural language processing because
digital news and social media content spread rapidly across languages. For Kazakh, the task is especially challenging due to
the limited availability of open labeled corpora and the weak adaptation of ready-made models to local media contexts. This
paper evaluates classical TF-IDF-based machine learning models on a balanced Kazakh-Russian fake/real dataset of 1,808
texts, with 452 fake and 452 real documents in each language. The experiments include bilingual training, Kazakh-only and
Russian-only evaluation, as well as Kazakh-to-Russian and Russian-to-Kazakh transfer. Word-level and character-level TF
IDF features are tested with Logistic Regression, Linear SVM, and Complement Naive Bayes. The monolingual and bilingual
settings achieved Macro-F1 around 0.985. Cross-lingual evaluation revealed a clear directional asymmetry: Kazakh-to-Russian
transfer produced Macro-F1 = 0.654, whereas Russian-to-Kazakh transfer reached Macro-F1 = 0.926. The findings are
interpreted as an explainable baseline for Kazakh-Russian fake/real classification rather than as a production-ready fact
checking system.
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