Identifying those at risk for depression is a crucial issue where social media provides an excellent platform for examining the linguistic patterns of depressed individuals. A significant challenge in depression classification problem is ensuring that prediction models are not overly dependent on topic keywords i.e., depression keywords, such that it fails to predict when such keywords are unavailable. One promising approach is masking, i.e., by selectively masking various words and asking the model to predict the masked words, the model is forced to learn the inherent language patterns of depression. This study evaluates seven masking techniques. Moreover, to predict the masked words during pre-training or fine-tuning phase was also examined. Last, six class imbalance ratios were compared to determine the robustness of masked words selection methods. Key findings demonstrated that selective masking outperforms random masking in terms of F1-score. The most accurate and robust models were identified. Our research also indicated that reconstructing the masked words during pre-training phase is more advantageous than during the fine-tuning phase. Further discussion and implications were made. This is the first study to comprehensively compare masked words selection methods, which has broad implications for the field of depression classification and general NLP. Our code can be found in: https://github.com/chanapapan/Depression-Detection.
Action editor: Myle Ott