The use of machine learning and deep learning models in detecting depression on social media: A systematic literature review

Depression is regarded as one of the world's primary concerns. Recent researchers use artificial intelligence techniques like machine learning and deep learning to identify depressive symptoms automatically. This literature review focuses on using machine learning and deep learning models in depression detection on social media. Advances in deep learning have improved methods for identifying depression, which is one of the illnesses that affect the health of individuals. Some researchers employ a variety of deep-learning approaches to improve the diagnosis, detection, and prediction of depression to support expert decision-making. The researchers identified the available prediction techniques and tools used to detect, forecast, compare, and classify depression in victims systematically. Twenty-eight (28) articles relevant to machine learning and thirty-two (32) articles linked to deep learning were chosen and considered using boolean keyword searches in different publishing databases and filters. A significant number of the studies, according to the conclusions of the analysis, used machine learning techniques such as decision trees, K-nearest neighbours, naive bayes, random forests, and support vector machines. The deep learning models that are most frequently utilised include convolutional neural networks, long short-term memory, and recurrent neural networks with different datasets to detect subjects suffering from depression using social media data. The datasets used in these studies include Twitter, Facebook, Reddit, tweets from the Kaggle website, and clinic patients’ records. These datasets can include posts, comments, audio, video, images, and interviews. The results of this study revealed that, recently, several approaches have focused on using deep learning for depression detection. The paper highlighted that most research focuses on the detection and identification of depression. Prospects for cutting-edge studies in the detection of depression and other illnesses that are related to health were also suggested.