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Fake News Detection in Albanian: A feature-based Statistical Analysis for a Low-Resource Language
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 84 |
| Year of Publication: 2026 |
| Authors: Elton Tata, Jaumin Ajdari, Nuhi Besimi |
10.5120/ijca2026926468
|
Elton Tata, Jaumin Ajdari, Nuhi Besimi . Fake News Detection in Albanian: A feature-based Statistical Analysis for a Low-Resource Language. International Journal of Computer Applications. 187, 84 ( Feb 2026), 35-43. DOI=10.5120/ijca2026926468
Abstract
Since fake information spreads so rapidly in digital media, detecting it has become a very important research problem. This study provides a statistical and feature-focused examination of fake news detection in Albanian. A total of 54 manually engineered linguistic, lexical, punctuation, textual statistical, structural, and temporal features are analysed to capture key aspects of language. The analysis is conducted using a balanced dataset of 3,994 Albanian-language news articles. Descriptive statistics, correlation analysis, and feature importance measures are employed to identify the most significant indicators of fake news. The findings show that sensational language, reduced lexical diversity, inadequate source attribution, and discrepancies between headlines and content are important predictors of fake news. The findings indicate that carefully designed, language-sensitive features can effectively identify disinformation patterns in Albanian news and provide clear insights into the detection methodology. This paper adds a framework for interpretable feature analysis that helps detect fake news in resource-poor linguistic environments. It also lays the foundation for future research on multilingual and disinformation-based topics.
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