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Suicide among adolescents continues to be a serious problem. Studies have found that substance use of tobacco, alcohol and illicit drugs is associated with suicide ideation and attempts. An article also analyzed gender differences in terms of suicide ideation and suicide attempts by considering substance use factors separately for males and females. The article is based on the data collected for the 2011 European School Survey Project on Alcohol and other Drugs (ESPAD), which was carried out in 42 secondary schools in Kosovo. A total of 4709 students born in 1995 and 1996 were surveyed according to ESPAD methodology. Logistic regression results indicate that cannabis use during the last 30 days was strongly associated with suicide attempts for both males and females. Frequency of alcohol consumption over the last 30 days was also associated with suicide ideation and attempts. The lifetime use of illegal drugs (amphetamines, ecstasy or hallucinogenic) was highly associated with suicide ideation for males but not for females. Findings show gender differences in suicide ideation; females reported higher rates of suicidal thoughts (9%) than males (6.3%). On the other hand, there were no gender differences for lifetime self-reported suicide attempt rates (3.4%). Findings from this article have a major relevance for youth suicide prevention strategies and action plans, emphasizing that not only the illegal drugs but also legal drugs such as, tranquilizers and alcohol are strongly associated with suicide ideation and attempts.

Automatic authorship identification is a challenging task that has been the focus of extensive research in natural language processing. Regardless of the progress made in attributing authorship, the need for corpora in under-resourced languages impedes advancing and examining present methods. To address this gap, we investigate the problem of authorship attribution in Albanian. We introduce a newly compiled corpus of Albanian newsroom columns and literary works and analyze machine-learning methods for detecting authorship. We create a set of hand-crafted features targeting various categories (lexical, morphological, and structural) relevant to Albanian and experiment with multiple classifiers using two different multiclass classification strategies. Furthermore, we compare our results to those obtained using deep learning models. Our investigation focuses on identifying the best combination of features and classification methods. The results reveal that lexical features are the most effective set of linguistic features, significantly improving the performance of various algorithms in the authorship attribution task. Among the machine learning algorithms evaluated, XGBoost demonstrated the best overall performance, achieving an F1 score of 0.982 on literary works and 0.905 on newsroom columns. Additionally, deep learning models such as fastText and BERT-multilingual showed promising results, highlighting their potential applicability in specific scenarios in Albanian writings. These findings contribute to the understanding of effective methods for authorship attribution in low-resource languages and provide a robust framework for future research in this area. The careful analysis of the different scenarios and the conclusions drawn from the results provide valuable insights into the potential and limitations of the methods and highlight the challenges in detecting authorship in Albanian. Promising results are reported, with implications for improving the methods used in Albanian authorship attribution. This study provides a valuable resource for future research and a reference for researchers in this domain.