Early Risks Depression Prediction of Indonesian Twitter Users Through Indonesian Text Using Transfer Learning and Linguistic Metadata Features Approaches

Depressive disorders are the first rank of mental disorders that cause DALYs (Disability Adjusted Life Years) with a death contributor percentage of around 14.4%. Currently, depression has been detected based on self-reported by sufferers, behavior reported by sufferers’ friends, and mental health examinations. This work was detecting depression based on social media data using machine learning approaches. Machine learning algorithms are regarded as effective nowadays for completing jobs involving natural language processing with vast amounts of tagged data. In contrast to English, the depression (Indonesian text) classification data set still has gaps. To overcome the issue we combined Indonesian and English data with three different experiment scenarios–monolingual, zero-shot, and multilingual–this study employs pre-train language models (Multilingual BERT and XLM-RoBERTa) and Linguistic Metadata Features.. In the course of the experiment, 55 relevant individuals and 4,465 Indonesian tweets were employed. Additionally, the performance of the model was enhanced by using 476 people and 51,005 pieces of Reddit data. The experimental validation data showed that the use of linguistic metadata features has no discernible impact on predicting depression’s user. The highest FI score obtained utilizing a zero-shot scenario, a multilingual BERT, and a learning rate of le-5 was 0.62. Additionally, an F1 score of 0. S15 is achieved in an XLM-RoBERTa fine-tuning scenario with a learning rate of le-5. It was successful in surpassing the baseline approach that was again used, with an F1 score of 0.615.